Much has been written about entrepreneurship, but the beginning of the process is the least explored and described. In fact, little has been written to describe how one identifies the business opportunity, which sets the theoretical size for the business. I have been thinking quite a lot about opportunities in what will be my third book on entrepreneurship and have identified ten types of opportunities that have repeated throughout history and have always been large "new" markets. The overall framework of such market opportunities falls into three categories:
- Human values
- Neuroscience (think Kahneman)
The reader should note that each category of opportunity represents a basic, almost primitive, understanding of human beings.
What follows is an early draft of a chapter on networks that comes from the section on complexity. I would appreciate any comments, including by email.
“So in the future, ideas will be the real scarce inputs in the world - scarcer than both labor and capital - and the few who provide good ideas will reap huge reward”
The Second Machine Age Brynjolfsson and McAfee
In the last chapter we learned that complex human systems, such as social and economic systems, are non-deterministic, adaptive, self-organizing systems that process and store information. The dynamic tension between exploration and exploitation makes a complex system adaptive. The behavior that cannot be ascribed to any individual part of the leaderless system is the emergent quality of complex systems, which is more easily understood in the context of networks, which are the subject of this chapter.
One classic example of complex systems in both biological and human systems is networks. The term network refers to the framework of routes within a system of nodes. A route is a single link that can be tangible or intangible between two nodes. Networks can be physically constrained, such as transportation systems, or non-spatial, such as certain social and economic systems.
Examples of networks and their role in the history of economic development is shown in this quote from Jean-Paul Rodrigue and Cesar Ducruet’s article, “The Geography of Transportation Networks”:
“Transportation networks have always been a tool for spatial cohesion and occupation. The Roman and Chinese empires relied on transportation networks to control their respective territories, mainly to collect taxes and move commodities and military forces. During the colonial era, maritime networks became a significant tool of trade, exploitation and political control, which was later on expanded by the development of modern transportation networks within colonies. In the 19th century, transportation networks also became a tool of nation building and political control. For instance, the extension of railways in the American hinterland had the purpose to organize the territory, extend settlements and distribute resources to new markets. In the 20th century, road and highways systems (such as the Interstate system in the United States and the autobahn in Germany) were built to reinforce this purpose. In the later part of the 20th century, air transportation networks played a significant role in weaving the global economy. For the early 21st century, telecommunication networks have become means of spatial cohesion and interactions abiding well to the requirements of global supply chains.”
Carlota Perez is a history of economics scholar who has devoted much of her research and analysis to understanding paradigm shifts or more simply put—technological revolutions. In a paper in 2004, “Finance and Technical Change: A-Neo-Schumpeterian Perspective”, she includes a graphic, shown below, that traces each of the major technological revolutions, starting with the Industrial Revolution in 1771. [graphic omitted]
If one examines each example of the “New or Redefined Infrastructures” (Column 3 above), almost every example is a network. If one accepts Perez’s analysis, this graphic clearly demonstrates the role of networks in the history of economic development and by extension in entrepreneurship. (Perez’s analysis shows all the network examples as infrastructure. The other common form of economic or social networks is a marketplace, which we discuss in the next Chapter.)
When one considers an explanation for the close link between paradigm shifts and networks, traditional economic considerations of production, distribution and consumption provide me with no insight. However, if we return to the insights of Ronald Coase, we see economic activity in a less traditional way as a combination of property rights [information], arrangements for collective choice [collaboration/feedback] and contracts for motivating managers and employees [social exchange/signaling]. [Footnote: Coase paper] Stepping back, what one realizes about economic networks is the efficiency a network provides. Networks provide connectivity, communication, operations and management, all in a self-organizing mechanism for information. Networks are nature’s answer to the Swiss Army knife.
Networks are such a “popular” and versatile mechanism for four reasons:
- Networks lower the cost of searching for information
- Networks lower the cost of verifying information
- Networks lower the cost of processing and storing information
- Networks lower the friction in exchanging information
Economic and social networks achieve these benefits in part through trust amongst participants, which we discussed in Chapter I. The further disclosure and transfer of information within the network builds the trust and fosters the organizing, processing and archiving of information. Trust also lowers transaction costs, thereby facilitating the construction of larger networks.
Herbert Simon writing on hierarchies [networks] cites three reasons why they are so common [Footnote: Simon 1962]:
- Networks facilitate the formation of complex systems (see Metcalf’e’s Law below)
- Networks have direct channels of communication (connectivity)
- Networks are naturally redundant (lower transaction cost)
Strengthening the versatility of networks is Metcalfe’s Law, which says that networks follow a scale-free power-law distribution. (Every additional node in a network increases the value of the network.) As Albert-L´aszl´o Barab´asi explains it, “This feature [power law] was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices [nodes], and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.” [Footnote: “Emergence of Scaling in Random Networks”]
Mitchell makes an interesting point about the size of networks:
“Self-regulation in complex adaptive systems continually adjusts probabilities of where the components should move, what actions they should take, and, as a result, how deeply to explore particular pathways in these large spaces.” [Footnote: Mitchell, Melanie (2009-03-02). Complexity: A Guided Tour (Kindle Locations 2952-2953). Oxford University Press. Kindle Edition]
Mitchell’s probabilities, what might in the vernacular be called uncertainties, are discussed in more detail by JK Galbraith:
"the greater the uncertainty of the task, the greater the amount of information that must be processed between decision makers during the execution of the task to get a given level of performance".
This rather simple observation explains the evolution of “organizations”, which is the subject of the next chapter, and leads to two observations:
- In small, resource constrained networks there is usually a large node or organization that dominates
- In large networks the need for a large, dominant node is reduced (because of the distributed information processing power)
This relationship between network and the number of organizations [node] explains why early U.S. colonies required a federal government. Conversely, with today’s large, global, interconnected networks, perhaps we can downsize federal government in the U.S.
The relationship between networks and entrepreneurship is only now emerging, mostly due to the growing fields of information theory and complexity economics. However, in some ways the practitioners are ahead of the academics in their understanding of this relationship. Notable venture capitalist Fred Wilson of Union Square Ventures sees the establishment of a network as a competitive advantage that prevents competition from entering a market. Peter Thiel of PayPal fame recommends that startups go after small markets where dense networks can be created. (Giulio Tononi’s Integrated Information Theory uses “dense network” as a measure of how much more a system is than the union of its parts.) Thiel sees networks as a mechanism to achieve monopoly, his preferred position in any market. Facebook’s eclipse of MySpace shows, however, that networks are not a panacea or invincible business model, An even better example of the network model is Google. Google’s search algorithm targeted nodes with a large number of connecting links, just what Barab´asi explained about networks when he said, “new vertices attach preferentially to sites that are already well connected”. The insight here is that the Google algorithm followed the pure theory of power laws and networks and the opportunity proved to be quite large. Google’s approach also used autocatalysis, a characteristic of some complex systems, where the product of the search reinforced the importance of the information in future searches
Academic research has shown that companies using the network business model create more shareholder value. In an HBR article, “What Airbnb, Uber and Alibaba have in Common” [Footnote: https://hbr.org/2014/11/what-airbnb-uber-and-alibaba-have-in-common], the authors analyzed companies in the S&P 500 over a forty-year period starting in 1972. Companies were categorized as one of four types:
- Asset Builders
- Service Providers
- Technology Creators
- Network Orchestrators
Companies that were network orchestrators showed “higher valuations relative to their revenue, faster growth, and larger profit margins”. The researchers also discovered that only five percent of S&P companies are network orchestrators. The authors explain the value creation, “We believe this occurs because the value creation performed by the network on behalf of the organization reduces the company’s marginal cost, as described in Jeremy Rifkin’s The Zero Marginal Cost Society.” Looking for a more network-oriented explanation, I would think that the scarcity of network operators perhaps shows the challenge of successfully building and sustaining a network model. The efficiency of network value creation perhaps demonstrates Michael Porter’s findings that the competitive advantage [in successfully building and sustaining a network] is a key requirement for extraordinary value creation.
As we look to the future and the market opportunity offered from our understanding of networks, “The Second Machine Age” perhaps provides some guidance:
“The winners are no longer those able to compete solely based on cheap labor or ordinary capital, both of which are being squeezed by automation. … Fortune will instead favor a third group: those who can innovate and create new products, services, and business models. … So in the future, ideas will be the real scarce inputs in the world - scarcer than both labor and capital - and the few who provide good ideas will reap huge rewards.”
The Manifesto 15 Handbook discusses a new pedagogy, but I believe it can be generalized to show a type of market opportunity:
“Our traversals across networks are our pathways to learning, and as the network expands, so does our learning. In connectivist approaches to learning, we connect our individual knowledges together to create new understandings. We share our experiences, and create new (social) knowledge as a result. We must center on the ability of individuals to navigate this space and make connections on their own, discovering how their unique knowledge and talents can be contextualized to solve new problems.”
Scientists have long believed in the power of networks to foster research and learning. The Royal Society in England was founded in 1660 to support understanding in science. My point here is not to foster the further development of learned societies but rather to show that scientists have viewed the world in a similar way to The Second Machine Age since 1660. The opportunities suggested to me by this expanded networking include increased outsourcing, more hands-on learning between masters and apprentices and more tools for curating information.
One of the most interesting and difficult to understand parts of complex systems is that all complex systems are emergent. Before proceeding further, we should heed Melanie Mitchell’s warning that what do not understand about a complex system is not necessarily an emergent characteristic. Emergent properties are characteristics or behaviors that cannot be explained by the leaderless system of independent variables. Some scholars explain consciousness as an emergent property. Others explain sexual desire as an emergent property. Facebook perhaps demonstrates an interesting emergent property of some networks. A report by the international audit and consulting firm Deloitteestimates that the economic impact of Facebook on a global basis in 2014 was [Footnote:http://www2.deloitte.com/content/dam/Deloitte/uk/Documents/technology-media-telecommunications/deloitte-uk-global-economic-impact-of-facebook.pdf] $227 billion, of which $29 billion was attributable to “platform effect”—third party apps and services that attached to the Facebook infrastructure. I believe that “platform effect” is an emergent quality that enriches both the original network and the third party extension. Another example of an emergent characteristic might be many authors joining a network of book readers where they can interact directly with the readers. Readmill, acquired by Dropbox in 2014, offered this feature. Perhaps the advertising revenue model of Google search is another example of a successful network with an identifiable emergent characteristic. Perhaps a greater focus on the emergent characteristic would have enabled Readmill to survive as a standalone company. Building networks to foster symbiotic emergent characteristics such as platform effect may be a large market opportunity. The platform effect at both Google and Facebook was an after thought, as would be expected based on complexity theory, but in fact a key to success in both cases. At Google it provided the means to monetize search and at Facebook it accelerated the network effect for Facebook (and probably drew the world’s attention to social media). A business based on a network without an emergent characteristic is by definition a failure as a network. Fostering emergent qualities in networks should be a big opportunity given that the number of potential networks will only increase with the proliferation of digital technology. Such opportunities could involve network design or perhaps services to encourage the emergent characteristic.
Another interesting opportunity related to the network effect is Bitcoin and the underlying Blockchain infrastructure. Originally I was totally enamored of the idea that Bitcoin would replace government as the monetary authority by eliminating the need for government–issued currency. (The notion of eliminating government control of monetary affairs is almost irresistible.) With more thought on the subject I think Blockchain is a potentially bigger opportunity. Blockchain allows the members of a network to collectively authenticate data, replacing the role of a central authority. The MIT Media Lab Enigma project, according to Fast Company, uses the Blockchain technology to “enable a marketplace where users can sell the rights to use encrypted data in bulk computations and statistics without giving raw access to the underlying data itself”. For example, personal health record data could be shared without revealing individual identities. Effectively the Blockchain technology creates trust, verifies the data and reduces the cost to a network of processing information. With the increased size, versatility and resources of current networks with support from Blockchain, perhaps the biggest opportunity should be to use the newfound power of networks to replace government.
Note: The Edward Snowden affair may demonstrate that we have more confidence in Google than the U.S. government, these two being the largest collectors in the world of personal information. Now if we could only convince the Communist party in China and the Republicans and Democrats in the U.S. to go along, we could let individuals combined with network and AI technology manage global affairs. Ever the idealist!
The last opportunity that may emerge is in services to networks. For example, a university wants to start offering educational tours in Africa to alumni as a means to add value to the alumni network (and hopefully increase donations). The university will need a wide range of services to execute a strategy outside classroom education. Another example comes from Blackrock, the asset management behemoth. Any company that Blackrock invests in can purchase travel through Blackrock’s travel supplier[s] and take advantage of the volume discounts. An interesting example comes from my hometown Miami Marlins. They have created a network to share business between their corporate ticket holders. Both Blackrock and the Marlins need services for the network to exploit this additional opportunity to create value. As network becomes a better understood method to add value to an existing business model, the need for network services should increase."