One especially valuable passage in the book is the accessible introduction to one of the most powerful economic theories of organization, Transaction Cost Economics (TCE). TCE emerged in the 1970s and its originator, Oliver Williamson, received the 2009 Nobel Prize in Economics, yet I have seen relatively little coverage of the theory in popular social science books. Put succinctly, TCE helps explain why organizations exist. It explains why some transactions are governed by market mechanisms (i.e. contracts) and others are governed by hierarchies (i.e. organizations), by analyzing how the relative costs of these competing forms of governance vary depending on the nature of the transaction. The large academic literature on TCE helps explain why firms shift toward or away from vertically integrated structures over time. Integration is favored when contracts are harder to write, for example when monitoring of effort or output is trickier or when rapid change generates uncertainty about the future.
Brynjolfsson and McAfee bring this theory to bear to examine what the emergence of digital platforms means for the size of organizations. The rise of Big Data reduces our uncertainty about transaction partners, as we know more about their track record. In addition, transaction platforms can incorporate tools to reduce the likelihood we get defrauded by counterparts we transact with over the platform. These factors lower the cost of using market mechanisms to govern transactions. More transactions can be governed by markets rather than hierarchies. As a result, digitization can drive down the size of organizations, and mean workers are increasingly treated as contractors rather than employees (a source of considerable controversy!).
The book is arranged in three sections each devoted to one of the phenomena mentioned in the title. (Machine here refers to machine learning technology and Crowd refers to decentralized modes of organizing). To expand on the book, one could undertake a structured analysis of how the three phenomena mutually interact and (potentially) reinforce one another. I’ve sketched out the beginnings of such an analysis in the Figure below. There are at least seven mechanisms generating synergies between machines, platforms, and crowds, several of which are mentioned in the book, and several of which I’ve added:
- Machine learning improves digital platforms through two main mechanisms. First, it improves the quality of search tools which allow digital platforms to make better quality matches between users and complements. This helps users access the long tail of products that are hard to provide in offline settings. Example: Amazon’s recommendation algorithm helps users find items they want to buy.
- Second, machine learning tools reduce costs related to processing transactions over platforms. Machine learning helps platforms verify identities and detect fraud.*
- Crowds contribute to the effectiveness of platforms. User generated feedback is an important basis for establishing the trust that allows transactions to happen on digital platforms. For example, users rate their Uber drivers—and drivers rate passengers. User feedback weeds out bad actors, thereby improving users’ confidence in the platform.
- Platforms, in turn, enable machine learning. McAfee and Brynjolfsson identify the five critical inputs of machine learning as (i) data, (ii) algorithms, (iii) networks, (iv) the cloud, and (v) hardware. Platforms directly improve machine learning tools by generating masses of data on user interactions. This data can be used to train algorithms to make predictions, which can feed into automated decisions made by the algorithm. For example, Google uses past data on who tends to clicks on what in order to optimize its ad-serving algorithms.
- Platforms can be used to run innovation contests, which employ the “wisdom of the crowd” to solve difficult technical problems. Innovation contests may be used for many types of problems but have proven particularly effective for firms searching for better algorithms. In this way, platforms and crowds jointly contribute a key input to machine learning. For example, Netflix used an innovation contest to find a better user recommendation algorithm.
- Machines and platforms empower crowds. Machines and platforms democratize entrepreneurship by providing tools for anyone to go about building their own business. Machine learning tools are not exclusively used by large firms: powerful tools such as TensorFlow have been made open source, meaning anyone may adopt them for free. Other tools, such as IBM’s Watson or Microsoft’s Azure, are available to entrepreneurs to incorporate as modules in their enterprise via an API.
- And, finally, crowdfunding platforms allow entrepreneurs to raise financial capital to support their business. Some crowdfunding is “reward-based” (e.g. Kickstarter) allowing the entrepreneur to effectively make pre-sales which then fund production, while other platforms exist for founders to raise equity or debt finance from a broad pool of small investors.
*Machine learning to improve fraud detection is discussed in some detail in the book Prediction Machines by Agrawal, Goldfarb, and Gans.