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Digital Media Advertising and Blockchain



Abstract

This academic paper analyses the role of digital platforms in our society from an economic perspective and focuses on blockchain technology as a potential solution to many of the issues that rely on the digital advertising market (e.g., inefficiency and concentration of market power). In the first chapter, I analyse digital platforms’ framework and the risks associated with their anti-competitive behaviour. I also describe the current digital advertising market and its highly fragmented value chain. The many intermediaries that participate in the value chain make transactions highly expensive and the outcome is poor in terms of browsing performances. After describing the flaws in the current digital advertising market, I compare blockchain-based solutions to the third alternative of the Coase Theorem, which gives an alternate framework to the allocation of property rights for the first time. In the second chapter, I introduce blockchain technology, token taxonomy and Ethereum protocol to later dive into the Basic Attention Token, which combined already represent a new infrastructure for digital advertising. In the third chapter, I dive into how the BAT works, using its whitepaper as the main reference. After analysing the technology, I make some final considerations on why BAT is to be considered a promising innovation, also pointing out some of the threats that it still has to overcome to become a mature and scalable solution.


We are the Bocconi Blockchain and Cryptocurrency Association. We foster a community-driven approach for the adoption of Blockchain and Cryptocurrencies. Our goal, as an association is to research the technology, to help other people learn about its mechanisms, potential application and challenges, and provide opportunities for discussion with experts and professionals in this broad new exciting industry. This paper is written by Andrea D'Amico, former member of Bocconi Students Blockchain and Cryptocurrencies Association

AUTHORS: Andrea D’Amico (ad3720@ic.ac.uk)


1. Digital Platforms and the Digital Advertising Market

Before diving into the digital media market and its peculiarities, it is of crucial importance to understand what a digital platform is. One of the main reasons why digital platforms’ business models involve many controversies is that there is not a unique and unambiguous definition of “digital platform”. Additionally, their business models expand so easily as to continuously increase the heterogeneity of the services offered, thus making it difficult to define the core business of digital platforms. Among the most popular digital platforms are Facebook and Google. In April 2018, Mark Zuckerberg told Congress that he considers Facebook a technology company rather than a media company. Even though Facebook builds enterprise software, Mark Zuckerberg does not consider his company an enterprise software company, and even though Facebook provides people with tools to send money, Facebook’s CEO does not consider his company a financial institution. Then how can we define Even though Facebook builds enterprise software, Mark Zuckerberg does not consider his company an enterprise software company, and even though Facebook provides people with tools to send money, Facebook’s CEO does not consider his company a financial institution. Then how can we define what a digital platform like Facebook does in principle? To consider all the issues that digital platforms are concerned with we need to embrace a broad definition of digital platform. The European Commission defines an online platform as an «undertaking to operate in two (or multi)-sided markets, which uses the Internet to enable interactions between two or more distinct but interdependent groups of users so as to generate value for at least one of the groups». Another broad definition is provided by Spagnoletti, Resca and Lee, who define a digital platform as “a building block that provides an essential function to a technological system and serves as a foundation upon which complementary products, technologies, or services can be developed”. It has been illustrated that even as the number of publications on the topic increases, there continues to be ambiguity in the conceptualization of digital platforms among Information System scholars. Hence, we will refer to a digital platform in technical terms not to exclude any type of operation performed by online platforms. This paper will emphasize the way by which digital platforms have become increasingly pervasive in our lives. On account of that, any technical definition thatunderlines digital platforms’ potential of exponential growth in correlation with their capability of offering complementary services can be considered sound for the scope of this paper. In the next chapters, I will analyse digital platforms’ framework and the risks associated with keeping centralized systems within the digital advertising market, making some relevant examples. What is wrong with platforms such as Facebook and Google? What should we worry about if, in the end, we can obtain answers to almost any query in a fraction of a second and chat with our relatives in real-time from opposite parts of the planet?


1.1 Benefits and Apprehensions


Since the 1960s, when the internet was born, innovation and globalization have increased at an incredible pace. The combination of the internet protocol suite (TCP/IP) and the World Wide Web has made it possible for governments, institutions and individuals to communicate within a network 2 that as of January 2020 counts around 4.5 billion users . Together with the internet, the application layer of technological innovation has been growing and many platforms have been shaping our lives. The business models of many enterprises have changed and those companies that could not keep the pace of innovation have ceased their operations. One of the first implications of increasing globalization and file sharing has been the enormous amount of data that companies and institutions started to deal with. On account of that, many firms have started to look for new methods and technologies to analyse big data with the intent of improving their decision-making process. Firms using Artificial Intelligent (AI) algorithms, Internet of Things (IoT) technology and Machine Learning have allowed us to live better. For instance, the start-up Whoop was founded in 2012 and it aims at optimizing sleep performance for individuals continuously measuring strain, recovery, and sleep. Its system relies on wearable technologies and personalized analytics. Machine learning algorithms allow Whoop to study customers’ heart rate variability (HRV) patterns and provide the company’s physicians with valuable insights about how to improve people sleep’s performance and health. Other start-ups such as Paypal and Revolut allow customers to make instant payments within a day. Amazon, initially presenting itself as an online library, now offers a wide range of services to individuals, companies and even governments. Amazon is not just an e- commerce anymore: since 2006 it has been offering cloud-based services to several companies through its Amazon Web Service (AWS) platform. In February 2020, Facebook offered to pay its users for personal information including recordings of their voice to develop speech recognition systems and to compete with rival Amazon’s Echo speakers. In October 2019, the US Department of Defence awarded Microsoft its Joint Enterprise Defence Infrastructure (JEDI), a cloud contract that will allow the company to handle a great part of the department’s data. These examples show how big-tech companies and digital platforms can improve our lives on the one hand, but also show the extent to which these entities are becoming increasingly pervasive in our lives. These pervasive business models have relevant implications for our privacy. In 2018 Facebook failed to protect millions of users’ confidential data that has been used by Cambridge Analytica to influence US political elections. This is just one of the major data breaches that have happened in this century. Cambridge Analytica also had a role in promoting pro-Brexit political campaigns in England in 2016. During the Covid-19 pandemic, the risks of privacy breaches have considerably increased, according to The Economist. Many governments have tried to use the pandemic itself as an excuse to grab more power. For instance, Cambodia’s emergency law allows for unlimited surveillance of private citizens. On the other hand, other governments are looking for scapegoats for the pandemic. In India, scapegoat equals Muslims. After it was discovered that a Muslim religious meeting was the source of 1,000 infections, the Hindu Nationalist government heavily publicised the fact. If we combine the tendency of governments and companies to influence consumers’ behaviour and opinions with the availability of our purchase patterns, opinions, habits, interests, and voice recordings it is easy to imagine how risky the outcome might be. There are other risks more directly related to the current framework of the digital advertising market. One type of risk is related to the higher costs associated with marketing campaigns within a significantly concentrated market. Facebook and Google together have the majority of the market share in the digital advertising business . Highly concentrated markets might lead to higher prices and reduced quality of the services offered. Hence, on one side, increased globalization and innovation have improved the quality of our lives; on the other hand, globalization and innovation have posed the premises for an extremely concentrated market power, which is associated with relevant risks and inefficiencies. In the last decade, there has been widespread debate over whether to introduce more severe regulation over the platforms that drive our lives. To understand how it could be possible to contain the risks and improve the inefficiencies coming from the digital advertising market, it is of fundamental importance to understand digital platforms’ business model. In the next chapter, I will introduce the characteristics that make digital platforms so powerful and risky at the same time.


1.2 Digital Platforms’ Business Model


This paper has been inspired by three valuable and independent reports about the need to introduce more regulation within the digital platforms’ market. These reports were published in 2019 by independent experts representing the European Commission (EU), the Digital Competition Expert Panel (UK), and the Stigler Centre of Chicago Booth University (US). Describing how digital platforms operate and how they influence both the economy and public policy, I will illustrate how blockchain technology could improve the current situation. It is important to keep in mind that the focus of this paper is not on whether digital platforms have had a positive impact on our lives. Indeed, as already discussed in the previous paragraph, nowadays most people are able to access services that we would have never imagined some decades ago. Rather, the main question that this paper tries to answer is whether the current centralized framework in which digital platforms operate should be changed to favour consumers and to constraint the risks associated with too much-concentrated market power. In a sort of parallelism, the real question is not whether we benefit from cars in our society, but whether we would put traffic lighters (external regulation) or roundabouts (auto-regulation) to reduce the risks associated with high speed. When the internet was launched and the World Wide Web was developed, the intent was to create a decentralized and layered communication protocol. Now there is even an acronym used to refer to the main platforms that influence and monitor our lives: FANG (Facebook, Amazon, Netflix, Google). Initially, it was expected that the internet would have increased competition among different service providers. Nonetheless, in part because of digital platforms’ M&A history and digital platforms’ nature, competition has decreased. One example is the combined indicative market share of two leading companies in the following selected UK digital market (Figure 1.A).


Since the ‘90s, the Digital Era itself has led to the creation of a limited number of “gateways” used by consumers to access a determined type of service. For instance, Google is the main search engine by which people search on the internet, WhatsApp, Facebook, and WeChat are the main platforms used by people to chat with one another, while Amazon and eBay are the main online marketplaces in which people exchange goods. But how have we arrived at this point? In order to appreciate how the market started to be so much concentrated, we need to point out some features of the Digital Era that inevitably affect market competition between digital platforms. ● Returns to scale: Returns to scale indicate a less than a proportional relationship between production costs and the number of customers served. While returns to scale are common among brick and mortar stores, for digital platforms this phenomenon is pushed to the extreme. For instance, once a search engine is active and fully developed, the costs of running it raise much more slowly than the number of customers using it. This feature has two main implications within the digital platforms’ market. The first one is that incumbents won’t enter a market unless they have much cheaper technologies at their disposition. The reason behind this argument is that with increasing returns of scale, the competition will not allow two platforms to offer the same product covering the costs of running their different networks. In fact, extreme returns to scale incentivize two platforms offering the same product to steal users from each other by reducing the price of the service being offered. In the case in which one of those platforms has already been in the market for some time, it will have lower costs than the other platform’s. Lower costs leave more room for the incumbent to lower the prices even more than the entrant. Under these circumstances, entrants will not join the market unless they can exploit a cheaper technology to have a competitive advantage over the incumbent. The second implication is that companies often charge zero prices to stimulate users to enter the network in order to increase their returns to scale. The main consequence is represented by a vicious cycle in which incumbents continue to expand their returns to scale.

Network externalities: this is the most common effect of every type of network. The more users participate to the network, the greater the usefulness of the network itself for its users. Would you have a Facebook account if no one used Facebook? The greater the platform, the more the users will find other peers to exchange value with. A meaningful example is represented by Skype’s network. Most people have been using Skype instead of Zoom because the former has always had much more users than the latter. Nonetheless, Zoom offers a better user-friendly experience in comparison with Skype. Because of networks effects, Zoom has not been widely used until Covid-19 introduced the need to constantly meet from remote. Hence, Zoom has become popular only once an external agent brought the focus on the technology, which was previously obscured by the advantage of being connected in the same platform in which most individuals used to meet. Metcalfe’s law underlies this principle . An advanced case of network externalities is represented by multi-sided markets. In these markets, the role of network externalities is even more relevant than in single-sided ones. Take Uber for instance: both drivers and customers exploit the platform to their benefits. The more drivers there are, the more users will be able to find lifts, thus the platform’s value will increase. In the same way, the more customers there are, the more drivers will be able to join the platform. The same concept applies to many platforms such as Airbnb, Steam, eBay, Switch, Amazon, and Shopify among others. Network effects and returns to scale may seem identical as for their impact on traditional competition. In fact, there is a subtle difference. Returns to scale incentivize concentration of market power because of technological factors. On the other hand, network externalities incentivize the concentration of users into one platform because of the difficulty to convince users of one platform to move in chunks to another one. Therefore, as we have seen with Skype and Zoom business case, network effects could even obstacle a superior platform to overtake an inferior one. ● The role of data: as previously mentioned, platforms often offer zero- price services to users to increase returns of scale and network externalities. This tendency incentivizes platforms to derive their revenues from advertising. When we talk about advertising, big data plays a crucial role. The more platforms know about their customers, the better they can target users with ads matching their preferences. This phenomenon has a greater potential in the digital advertising market than in the brick and mortar one. Think about a customer of a supermarket who is given a discount card that while being used allows the supermarket to track what the customer purchases. The customer could choose not to use the discount card or to lend it to another person. In this way, the data being collected would not reflect the actual preferences. On the other hand, online, each user is identified by a unique Internet Protocol (IP) address, which allows digital platforms to track each click and page viewed by the owner of the device used for navigation with much more precision. Once digital platforms track their users, they can create two main types of datasets: large population and high dimensional ones. Large population datasets are based on a significant number of observations and allow platforms to infer prospective users’ preferences. High dimensional datasets are composed of less but more detailed observations. The richness of the info collected in high dimensional datasets allows digital platforms to tailor ads according to consumers’ history of purchases or missed purchases (items add to the cart but finally not purchased). Both types of databases enable platforms to implement targeted advertising, thus making the network much more attractive to both advertisers and users. Take Facebook, WhatsApp and Instagram for instance. Imagine the insights that derive from all the information about what people like on Instagram and share on Facebook. Facebook can easily exploit extremely relevant datasets on which to base its target advertising. Considering the competitive advantage of Facebook as far as data management concerns, more and more advertisers are willing to use the renowned Facebook Ads algorithm to reach prospective customers. This phenomenon leads to a cycle that increases Facebook’s revenues, attracts more advertisers on Facebook, and “better” satisfies users according to their preferences. But is this a virtuous or an ultimately vicious cycle? After all, concentration of market power can often lead to unfair practices and higher prices. ● Economies of scope: the role of data stimulates economies of scope. The fact that digital platforms can collect users’ data to create datasets incentivizes platforms to use that data to diversify their services. Firms can implement cutting-edge technologies such as machine learning to improve the quality of inferences that derive from datasets analysis. In this way, tech-companies can also expand their activities into new areas. Remember how Amazon developed from an online library to a digital giant offering products ranging from one of the biggest online marketplaces in the world to a cloud storage service for enterprises (AWS). Ultimately though, the great growth potential of digital platforms might reveal to be a disadvantage. In fact, as explained by the Economist, Amazon’s growth rate is slightly decreasing in comparison to previous years. Slower growth might be related to some businesses refusing to adopt AWS. Owning high dimensional and large datasets that lead to extreme business diversification might ultimately be counterproductive because of competition coming from different segments. After all, why an online retailer should use AWS to storage its customers’ data? Using AWS, an online retailer would allow Amazon to handle its business data, giving Amazon a competitive advantage for free.


1.3 The Digital Advertising Market


Once we have outlined the main characteristics that make the digital platform market so unique if analysed from the perspective of traditional competition, we are going to focus on the problems that arise from the framework discussed in the previous chapter, with a particular emphasis on the digital advertising market. The main argument of this paper is that digital markets’ centralized framework has led to tipping in favour of the incumbents because of two main reasons: returns to scale and network effects. These factors induce digital companies to increase their market shares to beat potential entrants. The crucial role of market share in the business model of digital media companies is the main reason why thecompetition arena’s purpose has shifted from competition in the market to competition for the market. Hence, the primary problem is that once incumbents have obtained a large market share it becomes incredibly difficult for new entrants to gain room in the competition arena. In the previous chapter, I have outlined the features that pose the basis for the vicious cycle that not only excludes new entrants but also brings many inefficiencies in the digital advertising market. During the last two decades, the European digital advertising market has been increasing at an incredible pace. In 2016, the first ten countries for digital advertising expense invested from €2 to €14 billion in the digital advertising market (Figure 1.B). The UK is the country that spends most on digital advertising; in 2019 its spending went beyond €15 billion. Already in 2018, in the UK, traditional print advertising accounted for 9% of total media spend .



Figure 1.C illustrates the global spending on digital advertising, forecasting it until 2023. As shown by the graph, the digital ads will total 60% of the overall advertising market share.


Many journals such as the New York Times have changed their business models from traditional advertising to consumer-direct and subscription- style service. The incredible shift towards digital advertising combined with new technologies such as AI and Machine Learning used to create precise datasets has led advertising to follow audiences. Some time ago it was the opposite: audiences followed advertising that was placed next to the best quality content. Furthermore, during the last two decades, the framework of the digital advertising market has been complicated by the many actors that have joined the business in order to better target online customers. There are five main actors in the digital advertising market: advertisers, advertising practitioners, media owners, ad tech businesses, and online platforms.

  • Advertisers: these are entities or organizations that pay for advertising. Advertisers might be public, private or non-profit organizations

  • Advertising Practitioners: these are known to be the agencies that create advertising content to be published accordingly to ad campaigns tailored to the need of advertisers

  • Media Owners: these can be digital platforms that offer space on their websites and apps to advertisers. Examples are media publishers, broadcasters, and filmmakers among others.

  • Ad Tech Businesses: these companies are primarily concerned with delivering the advertising content to media owners by exploiting AI to reach advertisers’ target and prospective buyers

  • Online Platforms: these entities represent the biggest category of actors, and they include search engines, video-sharing platforms, social media companies, and text messaging apps, for instance. Online and especially social media platforms, can act as media owners, publishing advertising content at a price. Google and Facebook keep the biggest market share as media owners.

Companies and especially small players, who are not as vertically integrated as bigger players, engage with advertising practitioners to create advertising content such as pop-ups, videos, and banners. Once the content of the advertising campaign is set, ad tech businesses act as an exchange to target online customers on the internet. Ad tech businesses refer to media owners and online platform to reach the largest possible share of target consumers, according to the advertising campaign set by advertising practitioners. Ad practitioners might contact directly the media owner to buy online “inventory”, which is the space available for advertising on media owners’ websites. In fact, the main reason why ad practitioners do not directly arrange ad campaigns with media owners is that advertising is highly automated. Programmatic advertising is the process used to refer to the sale of advertising space on publishers’ websites. There are three key players in programmatic advertising: ad exchanges, supply-side platforms (SSP) and demand-side platforms (DSP). An ad exchange is a digital marketplace that enables the advertiser and the media owner to buy and sell advertising space through real-time auctions. DSP and SSP are respectively platforms that help advertisers to join different ad exchanges and publishers to gain the best yield from the sale of their advertising space. The steps through which programmatic advertising happens are the following:

  1. Advertisers decide which type of audience they want to target. During this phase, advertisers build their campaigns based on the particular characteristics of the consumers that they want to target

  2. Publishers make their advertising space available through SSPs

  3. An individual visits a page and her or his information is collected and gathered by the ad exchange. The info on the particular individual is available to the ad exchange thanks to the many third-party cookies and GPS tracking systems that consumers allow while navigating the internet

  4. If the characteristics of the consumer gathered match the ones established by a particular advertiser, that advertiser joins an auction. During the auction, all advertisers involved bid a price for the space available on the publisher’s website

  5. The advertiser that bids the highest price on the space available wins the auction and gets its ad published on the media owner’s website

  6. Finally, the publisher gets paid

Other actors support the advertising business and offer complementary functions to companies; examples are customer relationship management (CRM) platforms, data management platforms, analytics platforms (which track the user’s path within a website). Hence, the framework that holds up the digital advertising market is very complicated and fragmented. Many times the advertisers do not even have much control over their ad placement and on their ad expenses. A more complete picture of all the intermediaries that typically play a role in the digital advertising market is illustrated by figure 1.C.


1.4 Issues


It goes without saying that such a complicated value chain often leads to many negative aspects both for advertisers and publishers. • Transparency: the first negative impact that comes from a complicated infrastructure is the lack of transparency. Figure 1.d illustrates how much of the pie intermediaries get from one dollar of ad spending. In the worst-case scenario, the publisher gets 30% of a dollar.


Measurement issues: another problem that arises from the complex supply chain of the digital advertising business is the difficulty to measure results. Which metrics should be used to appreciate the effectiveness of advertising spending? The return on investment (ROI) of digital marketing campaigns becomes difficult to measure. For instance, many do not agree on the amount of time upon which a user is considered to have viewed a video ad. Some say that the optimal time is 50% of the video, while others sustain that a user retains the video ad content only if she or he watched 100% of the video. ● Advertising misplacement: the extreme aim of targeting and reaching as many users as possible often leads to misplaced advertising. In a piece of written evidence, Sky UK told the house of lords Communications Committee that in February 2017 one of their adverts was placed next to a YouTube video uploaded by David Duke, a white supremacist . As shown by figure 1.D, content providers also receive a stake in advertising spending. Hence, Sky UK pointed out that their ad spending could have potentially appeared to the public as a “funding model for illegal content”. Since then, advertisers have taken actions to avoid advertising misplacement. One of the most recent issues concerns the civil rights movement that has led many companies to stop their ad spending on Facebook. The campaign is called “Stop the Hate for Profit”, and some big brands such as Unilever and Verizon have already joined the movement. Other brands are deciding to boycott their ad spend on Facebook due to the company’s inactivity to address hate speech on its platform. According to Investopedia8, Facebook makes 98.5% of its revenue out of the digital advertising market. Nonetheless, the majority of this revenue comes from small and medium-size direct to consumer (DTC) brands such as Glossier and Casper, who are not joining the boycott. The paradox is that such brand are seeing a drop in the cost of Facebook’s ad inventory because of declining demand from other big brands that are joining the boycott. Hard to believe, the boycott is actually making ad spending on Facebook cheaper, without significantly harming the company’s revenues.

Advertising Fraud: where there is opacity, there is room for fraud. There are four main types of ad fraud.

  1. Invalid Traffic: clicks from non-human entities or “bots” on the web

  2. Malware: softwires that simulate or repeat clicks and views of videos

  3. Inventory Fraud: advertising space is sold to illegal entities who advertise illegitimate activity on the web and who imitate the legitimate URL of a media owner

  4. Infringed Content: legitimate advertising content that is transferred to a fraudulent website and that creates revenues for the fraudster In 2017, the News Media Association announced a scam called “the Methbot”, which defrauded advertisers of between $3 million and $5 million a day at its peak via half million fake users and 250,000 fake websites.

Lower-quality User Experience: each of the transaction in the complex value chain of the digital advertising market involves data transfer, which adds latency to the network. Furthermore, each transfer consumes the battery of users’ devices. The final outcome is a confused webpage composed by several ads that often do not catch the users’interest and that considerably reduce the quality of their experience on the web.

Privacy Externalities: as explained in the previous paragraphs, the digital advertising market is heavily based on the use of users’ data in order to make advertising more efficient. The use of data from advertisers involves externalities that arise from the information asymmetry that lies between companies and users. It is difficult for consumers to know how their data is used by advertisers, to monitor advertisers’ use of data and to discover any eventual violation. Most companies avoid the cost of accounting for their use of data just as other companies do not account for the pollution they cause in the environment. Both examples involve externalities that make different markets inefficient. In the case of digital advertising, the market has not only failed to do discipline companies that misuse personal information but has created an incentive for those companies to over-disclose such data to third parties.


1.5 Pathways

The outcome of the complex and fragmented infrastructure discussed so far is a highly concentrated digital advertising market that offers low-quality advertising to consumers and leads to significant externalities. The main views on how to counter market concentration and misuse of consumers’ data have focused on either self-regulation or government intervention. From the point of view of this paper, self-regulation is highly unlikely. Since the advent of the internet, competition has decreased and concentration has increased in the digital advertising market. As previously shown, Google and Facebook now detain the majority of the total market share in the digital advertising market. Furthermore, digital platforms are very good at exploiting consumer biases and switching costs to retain their consumers and to reduce competition even more than it is already reduced by the nature of the market. Taking into consideration digital platforms’ business models and the role of data in the digital advertising market, there is no means by which media owners such as Facebook and Google should autoregulate themselves to favour competition. On the other hand, also external regulation might not be the solution to the problem. Antitrust laws are often difficult to implement. Moreover, in the digital and social media industry, it is very difficult to measure the potential impact of a merger or acquisition on the market structure. On account of that, mergers that increase the concentration of the digital advertising market might continue to happen Many also sustain that the costs of regulating the market might outweigh the benefits of the Information Age. Thanks to Google’s use of consumers data, almost anyone can obtain a considerable amount of information in a matter of milliseconds. Opponents to over-intervention on digital platforms’ market sustain that constraining media platforms’ use of data might reduce the quality of the content they provide. Another point worth noting is that market tipping and resulting market power is durable, so while increased regulation might be useful to avoid future dominant incumbents, it is unlikely that it will improve the current situation. Last but not least, antitrust laws take a long time to be changed. Several steps involve the design and acceptance of new laws, and enforcement does not move quickly either. Self-regulation and government intervention are the two approaches that have been discussed so far in several states. Nevertheless, there is a third alternative that has not been considered so far. This alternative is best represented by a model first introduced by Coase in his 1960 paper “The Problem of Social Cost”. The Case Theorem has been widely used to face the problem of pollution externalities, and it could be interesting to apply it to privacy externalities as well. In one of his papers, Paul Sholtz discusses the Coase Theorem in the context of privacy as a problem of social cost. Before Coase’s work, there were two approaches to counter pollution. The first one, was direct oversight from the government in the form of industry regulation. In the case of the digital advertising market, government oversight presents various problems, the first of which is the hidden costs that must be bore by taxpayers to constitute an independent digital authority that would monitor anticompetitive behaviours. Another issue is represented by the actual reliability of a potential digital authority. To be effective, an independent authority should be deeply informed of the market practices to seek the public good. The current digital advertising market is so complicated that it would be difficult to follow all the transactions in details. Finally, it is also worth considering that a central authority might not always want to seek public good as it could be influenced by lobbyist groups. The second popular approach to counter pollution before Coase’s work was to impose commissions to the firm that released bad chemical compounds in the air to make it internalize the hidden costs of externalities. This approach assumes that the government, before imposing fees on factories and companies, can measure the environmental impact of production. While we are always closer to measure the environmental impact of many factories, we are not even close to measuring the impact of personal data’s misuse. There are so many factors to consider (the impact on the democratic process, for instance) that a model that tries to predict the effect of market concentration and privacy infringement does not exists yet. There is a hidden advantage of the second approach, which supports self-regulation and less government intervention (government only acts as tax collector in this second case). In fact, in the case of privacy, there is a corresponding financial pressure from media’s attention to personal data misuse that incentivizes self-regulation. As mentioned in the previous paragraphs, the boycott that Facebook’s ad misplacement has caused and the more important Cambridge Analytica scandal have harmed Facebook’s reputation, causing many big brands to take distance from the platform. Nevertheless, where the majority of Facebook’s ad revenues come from small and medium size DTC brands, we have seen that the negative financial impact on Facebook’s revenues is not significant. The Coase Theorem shows that there is a third alternative that is based on the redefinition and reallocation of property rights. The fundamental principle underlying this model is to understand that social costs have a reciprocal nature. This means that the externalities do not derive from a unilateral decision of a factory to pollute, but from a reciprocal decision of a fabric to pollute and of surrounding people to live near the fabric. Of course, it would not be a plausible decision to move an entire city away from a factory. On the other hand, there are smaller scale cases in which this kind of solution is feasible. Consider the case of a golf court, for instance. If the costs associated to moving the golf court away from an industrial area were less than the costs associated with imposing taxes on that area, moving the golf court would be the most efficient solution. In the case of privacy, we have a similar situation. Understanding that privacy externalities derive from a joint decision between the consumer and the media owner is of fundamental importance because assigning liability to one particular party is correct only if it can eliminate the externality at the lowest possible cost. In the digital advertising market, assigning the liability only to media owners and advertisers is not probably the optimal approach, as both self-regulation and an independent digital authority would imply considerable costs. Hence, the next chapter of this paper will consider blockchain technology as the potential third alternative examined by the Coase Theorem. Blockchain would eliminate the issue of market concentration as it involves decentralized protocols that do not have a unique point of failure. Furthermore, blockchain protocols allow to attribute property rights to customers, enabling them to maintain anonymity in transactions of whatever type. This technology might be the solution to the excessively concentrated market of digital platforms and to many of the inefficiencies that derive from the fragmented infrastructure of the digital advertising market. A great application of Blockchain within the digital advertising market is represented by the Basic Attention Token (BAT) and Brave Software. Based on blockchain technology, the BAT enables users to play an active role in the type of advertising content that they might want to view and implements a fair reward system for both advertisers and publishers. In the next chapter, I will introduce blockchain technology and discuss BAT and Brave as the alternative decentralized framework of digital advertising market.

2. The Underlying Infrastructure


Before introducing the Basic Attention Token and Brave Software as an alternative solution to the great concentration of market power in the digital advertising market, it seems reasonable to talk about the underlying technology that allows Brave to work. The original blockchain protocol was officially born in 2008 thanks to a computer scientist known by thepseudonym “Satoshi Nakamoto”, who invented Bitcoin. Bitcoin represents the first fully functional application of blockchain technology. Bitcoin protocol is an application of blockchain as a Peer to Peer (P2P) system for exchanging money in the form of digital tokens. Throughout the last decade, the technological infrastructure (i.e. computing power, mining pools...) that allows blockchain protocols to work has evolved and consequently many other applications of blockchain technology were born. For the purpose of this paper, it is important to clarify what is the scope of this technology and which are the forms (digital tokens) in which it has evolved since the Bitcoin exodus. The idea behind blockchain technology actually dates back to roughly 30 years ago. In many cases, Blockchain is still closely associated with a distributed payment system because the technology itself was first conceived with the Bitcoin protocol. But Bitcoin was the outcome of a more general trend that started in the early 1990s with the cypherpunk movement. The cypherpunk movement was initiated by a small group of mathematicians and computer scientists who wanted to give primary importance to privacy in the electronic age. Eric Hughes, one of the founders, wrote the Cypherpunk’s Manifesto, which expresses the main idea of the movement : “We the Cypherpunks are dedicated to building anonymous systems. We are defending our privacy with cryptography, with anonymous mail forwarding systems, with digital signatures, and with electronic money”. With this idea in mind, several developers have tried to build a protocol that could allow individuals to exchange money without being controlled by central authorities such as banks and other financial institutions. Ecash, Hashcash, B-money and finally Bit Gold were all attempts to create a distributed and anonymous payment system before Bitcoin. The previous versions of Bitcoin had some issues in their protocols that did not allow the network to reach either integrity or full disintermediation of transactions. For instance, Ecash represented the first form of digital cash in history and was implemented by applying a unique signature to digital money issued by a bank. Even though Ecash was later updated in 1999 with the cryptographic scheme known as Merkle Tree, it still needed a central bank to authenticate the signature applied to digital money. In 2008, Satoshi Nakamoto collected all the components of the previous attempts to build an independent network and finally conceived Bitcoin. The dream of the cypherpunk movement had eventually become true. Today any peer with a crypto account can simply connect to the internet and send bitcoins from one part of the world to another one, without the need to disclose his or her identity. The word Blockchain became popular only after the Bitcoin protocol became known. Since 2008, more and more developers have been attracted by Bitcoin’s framework. Many computer scientists understood that the disintermediation of Bitcoin’s protocol could have been implemented to any type of ownership change and not only to digital money. From there on, the attention shifted from Bitcoin to the underlying technology. In this chapter, I will illustrate what types of applications have stemmed from the original idea of the cypherpunk movement. First of all, a blockchain can be defined as a distributed database (accessible to anyone participating in the network) structured in blocks. All the blocks constitute the distributed ledger in which transactions are registered from the genesis of the network. It is worth noting that there are different types of blockchains (private or public and permissioned or permissionless) that depend on the restrictions imposed on the peers taking part in the network Illustrating the types of blockchains goes beyond the scope of this paper; by the word blockchain, I will always refer to the public protocol of the technology. On the other hand, the distinction between a standalone blockchain network and a dependent one is of fundamental importance to introduce the BAT. Blockchain can be interpreted either as a ledger or as an application. Metaphorically speaking, we could compare blockchain as a ledger to an operating system and blockchain as an application to specific software. The software works only if the underlying operating system guarantees integrity and security. Most of the consensus algorithms that fuel blockchains become more secure when more peers participate in the network. Since Bitcoin is the oldest protocol and has been adopted by an incredible number of users, it guarantees a good level of security as a ledger. Several blockchain applications have exploited bitcoin’s ledger and cryptography to transfer digital tokens within their networks. An example is Mastercoin: just as HTTP runs on top of TCP/IP, Mastercoin started to run on top of bitcoin’s protocol in 2013. Mastercoin was created to increase thelevel of security of bitcoin, without changing its protocol, and to financially benefit bitcoin holders. Through Mastercoin’s protocol, bitcoin’s users could start to exploit specific smart contracts that enable the exchange of smart properties such as bonds, stocks, and real estate among others. Hence exchanging Bitcoins for Mastercoins allowed users to join broader markets and financial resources that increased the utility of Bitcoin itself. The increased utility of bitcoin has led to more and more users joining the network and fuelling a virtuous cycle that both incentivize adoption and security.


2.1 Taxonomy


Just as Mastercoin, many other digital tokens stemmed from bitcoin’s blockchain as well as from other blockchains. Digital tokens can be classified on the basis of their scope in the IT infrastructure. Chris Burniske and Jack Tatar classify crypto assets (digital assets of which the exchange is governed by cryptographic algorithms) in three main categories:

a. Cryptocurrencies: including all the types of digital tokens under this umbrella is a common mistake and would underestimate the potential of Blockchain itself. In order to distinguish cryptocurrencies from other digital tokens, we ought to remember that Blockchain can be exploited to keep track of a wide range of ownership transfers. One kind of ownership transfer encompasses digital money. In this case, the digital token that is used as means of exchange within a blockchain network is called cryptocurrency, the most popular example being Bitcoin. Nonetheless, many other cryptocurrencies have born in the last decade; some other examples are: Litecoin, Ripple, Dogecoin, Auroracoin and Dash, among others.

b. Cryptocommodities: broadly speaking, commodities are raw materials used as a basis to create finished products. Likewise, crypto-commodities are digital tokens built on top of a (usually) standalone blockchain used to provide the infrastructure for other digital tokens to work. Examples of commodities in the digital world are computing power, storage capacity, and network bandwidth, for instance. The most popular crypto-commodity is Ethereum, which provides other digital tokens with computing power to execute transactions within their respective networks. The power of Ethereum is its smart contract16 infrastructure, which allows to create many types of transactions. In this way, Ethereum furnishes the infrastructure needed by other digital tokens to execute different ownership transfers, ranging from prediction market exchanges (Augur token) to digital advertising remunerations (Basic Attention Token), among others. c. Cryptotokens: these are also known as utility tokens, as they have an application that satisfies a specific need. In the real world, a token might be whatever physic item allows you to participate in a determined network and to access a particular service. For instance, a simple rigid bag bought at a mall can be considered a token. When you pay for your grocery shopping, you might decide to buy a rigid bag to bring the items more safely. Once you have bought the bag, you have acquired the right to use it to store and bring with you any other grocery shopping. You might also decide to sell the bag at a price. In this last case, you would lose the utility of the bag (token) and therefore you would exit the network composed of all the people who possess such a bag. The same dynamic applies to many clubs. When people pay to attend a night event, often the organization provides them with a ticket that they can use to get a drink at the bar. That ticket can be considered a token as well since it gives individuals the right to use it to obtain value from the network (in this case the value coincides with a drink). That token might also be sold by a person who does not drink alcohol at all. Crypto-tokens work in the same fashion; once bought, they give individuals the right to get a certain utility from the network they have joined. Bitcoin is a token that allows users to transfer monetary value, hence it is considered a cryptocurrency. Ethereum allows participants to execute transactions of any type, by offering the right to acquire computational power. Since computational power is considered a commodity in the digital environment, Ethereum represents a crypto-commodity. All other kinds of digital tokens that offer the right to access a specific service are called crypto-tokens. BAT is used to remunerate advertisers and users that navigate the internet using Brave browser, which is comparable to any other browser like Firefox or Safari, the main difference being that Brave is decentralized. Brave derives its name from the main company that is involved in the development of BAT: Brave Software.


With this taxonomy framework in mind, I will dive into how the BAT works and how it could revolutionize the digital advertising market in the next chapter.


2.2 Ethereum Protocol


As already mentioned in the previous paragraph, BAT has been issued on top of Ethereum’s blockchain and exploits Ethers to register transactions. Ethereum is considered the first decentralized supercomputer. The reason behind that definition is that Ethereum’s blockchain has been able to efficiently execute Nick Szabo’s smart contracts in a decentralized and scalable fashion for the first time in crypto asset's history. Furthermore, the reason why Ethereum came into existence was to create a token with a wider use case than a cryptocurrency. There are many differences between Bitcoin and Ethereum. Bitcoin developers have never wanted to complicate Bitcoin’s infrastructure by modifying the protocol’s rules because any enhancement would have represented an opportunity for hackers to find a new loophole in the code. Thus, keeping its original format, Bitcoin can maintain a high level of security. Some developers built Counterparty on top of bitcoin’s blockchain with an objective like Ethereum’s. Counterparty uses bitcoin’s blockchain to execute diverse smart contracts among its peers and is consequently subject to the same limits of bitcoin’s protocol. In other words, even though Counterparty enables individuals to perform various types of ownership transfers (depending on the kind of digital asset being exchanged), a transaction will always need around 10 minutes to be confirmed. The fact that Counterparty’s token (XCP) exploits bitcoin’s blockchain inevitably constrains the potential of a smart contract itself. For instance, a transaction made on top of Ethereum’s blockchain usually takes between 15 seconds and 5 minutes to be processed. These kinds of differences have a huge impact on the operations of any business applications. Broadly speaking, since Bitcoin was conceived to maintain a high-security standard as a Money over Internet Protocol (MoIP), there is less room for enhancements that have a different scope. Hence, around two years after the issue of Bitcoin, Vitalik Buterin decided to build an entirely new and standalone protocol and to exclusively focus on the improvement of the computational effort made to execute any kind of smart contract- driven digital asset transfer. In 2013, Buterin proposed Ethereum as the next-generation decentralized computer and in 2014 he and other developers set up the first crowd sale of Ethers. Ethereum’s protocol and blockchain are also known together as the Ethereum Virtual Machine (EVM), execute transactions for many types of businesses, serving as the hardware and software for decentralized applications (dApps). To make it simple, think of the Apple store: any developer can create an app and upload it on the store. Applications on the Apple store do not need their operating system, they rather use iOS. Just as Counterparty utilizes Bitcoin’s blockchain to register transactions, dApps like Brave exploit Ethereum’s blockchain to keep track of token exchanges. Users of Brave and other decentralized platforms have to pay a fee for performing a transaction. This fee is paid in the form of Ethers and the amount is often called “gas”, as it serves to execute ownership transfers that fuel each dApp ecosystem. Ethereum is open source, meaning that every voluntary developer can contribute to changes in its code and rules. As one of the fundamental cornerstones of any public blockchain protocol, no one can decide to modify the history of transactions on Ethereum’s blockchain. Another core principle of Ethereum’s blockchain is that smart contracts executed on top of its protocol are cryptographically secured. In this way, the identity of users who exchange digitalized properties is never disclosed. Peers participating in the network are identified by a public address (or alphanumeric number) that allows them to receive tokens in exchange for a specific digital asset, of which nature depends on the business application under consideration. For instance, in the case of BAT users can pay publishers for displaying interesting advertising material. The immutable nature of blockchain ecosystems implicates a good level of security within the network, even though it might reveal to be a drawback under external attacks such as in the case of the DAO scandal. It is worth nothing that the more complex a protocol, the riskier it is to participate in the network: as previously explained, complexity is inversely proportional to security in the digital environment. The risk of participating in a network such as Ethereum’s became evident in 2016 when some hackers took control of 3.6 million ethers from a decentralized application known as Decentralized Autonomous Organization (DAO). At that point, Vitalik Buterin and the developers and users of the Ethereum community decided to create a second history of transactions to give the money equivalent back to the investors and users of the DAO. This (contested) decision prompted a hard fork of the original blockchain that led to two different blockchains: Ethereum (the old version) and Ethereum Classic (the new version). This event is particularly important to understand the trade-off between the security and scalability of a blockchain network. On the one hand, Ethereum allows to efficiently execute any kind of digital asset transfer on its blockchain. On the other hand, its network reveals to be less secure than Bitcoin’s.


3. The Basic Attention Token


3.1 The Attention Market


As illustrated in the first chapter, advertisers and publishers participate in an excessively fragmented digital advertising market that eventually leads to high transaction costs and latency. The argument of this paper states that a new and distributed infrastructure is needed. Since the current digital advertising framework is misplaced and market players have lost the focus on the actual attention paid by users, there is also the need for a new unit of exchange. To solve this problem, Brendan Eich, founder of Mozilla browser and JavaScript language, launched the Basic Attention Token and Brave decentralized platform in 2015. The latter is a digital platform that poses the basis for a new framework, while the former represents the catalyst that allows the browser to operate. In other words, Brave is a browser like Mozilla, Internet Explorer and Safari, for example. BAT is a token issued on top of Ethereum Blockchain, and consequently, it exploits Ethereum’s computational power; this has both positive and negative consequences as for the token’s operations. We will discuss more pros and cons in the next paragraphs. However, the great potential of BAT is that it directly addresses what should be considered the core of the digital advertising market: users’attention. It also connects advertisers, publishers and users in an incredibly efficient way. Every user is confidentially tracked only by Brave and not by third-party cookies so that developers participating in the network can create rich metrics for user attention. For instance, when navigating on the internet, Brave records the amount of time of active user engagement with impressions or banners (that users decide to visualize). One of the main difference between Brave and other internet browsers is Brave’s in-device machine learning. While external and third-party sources are often unable to track users well enough not to display ads for items that they have alreadypurchased, Brave’s machine learning technology does an incredibly better job. Meaningful attention metrics ensure that user attention is genuinely measured and monitored so that the ad that users will visualize is the one with the greatest probability of converting them into a transaction. One example of Basic Attention Metric (BAM) is the “concave” score, which “rewards publishers for a threshold and bounded function of the amount of time spent with the open and active page”. In this way, publishers are given a certain score based on the amount of time that users spend in actively engaging with their page accordingly to a discontinuous function (Figure 2.A). For instance, for a threshold of a 25 second-view, publishers are given one “point”, and so on. The points cumulated by publishers and their overall score will be used to determine the number of BATs that they will receive for providing high-quality content.

Another Basic Attention Metric that assigns a score to publishers is 5 views of advertising content for at least 5 consecutive seconds. These types of metrics would be better calculated on a 30-day window.


3.2 The Technology


Under the Brave ecosystem, advertisers will give publishers a reward in BATs of which the amount will depend on the user attention. As already explained, user attention is measured by more and more indicators introduced by publishers and users themselves. Figure 2.B shows the value chain of Brave browser, which is prompted by BAT rewards.


In the status quo, advertisers pay publishers to promote their products and try to convert user attention in purchases. Within Brave, the mechanism is similar albeit arranged to incentivize improvement of the network. When users see an ad (e.g., an impression), they receive a reward in BATs from which they keep some and donate the rest to publishers who eventually receive their revenue for having displayed the ad. This flow can be represented by the following equation: 𝑃𝑢𝑏𝑙𝑖𝑠h𝑒𝑟 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 = 𝑋𝑝 = 𝑋𝑎 − 𝑋𝑢 − 𝑋𝑏


  • Xa = Advertiser buy-in

  • Xu = User share

  • Xb = Brave placement share


A part of the payment from the advertiser is retained by Brave browser to perform the transaction in the form of a smart contract on top of Ethereum’s blockchain. It is now clear how BATs represent the catalyst of the Brave network. After receiving part of the ad payment, users become a core element of the ecosystem. They can use BATs to pay publishers’ premium services, for instance. Since users receive BATs in the form of a reward, they will find it easier to spend it on publishers’ premium subscriptions. Besides, publishers themselves could use the number of BATs that they receive from advertisers to offer free premium subscriptions to users; this new business model would increase the revenue for premium content providers. By contrast, individuals usually tend to avoid premium versions in the current digital advertising market. BATs could be further used to increase the quality of the advertising videos or images displayed by premium content providers. Broadly speaking, BATs use cases depend on the types of applications created by developers of the BAT community. The fact that Ethereum and thus BAT’s source code is open-source enables any developer not only to start using Brave but also to create new applications of BAT. Hence, tokens could be used to rank and reward the most useful comments and reviews on a particular product, or to buy the right to comment to avoid abusive commenters. BATs may be used also to purchase high-resolution photos or videos, cloud-based services, and anything that includes a right to be used (remember the term utility token). Brave aims to eventually create an ecosystem where digital goods are exchanged and genuine advertising of them is incentivized. Another factor to consider is privacy. Brave makes privacy one of the most important priorities for the network’s performance. Transactions within the ecosystem are recorded on Brave Ledger System, which is a Zero-Knowledge Proof scheme that allows users to keep confidentiality while navigating the internet. In other terms, Brave privately monitors and measures engagement with advertising content by tracking anonymous alphanumeric addresses (uniquely associated with peers in the network) rather than users’ actual identity. Moreover, Brave blocks third-party cookies that normally follow the users’ path during the Hypertext Transfer Protocol (HTTP) sessions. To summarise, in-device machine learning carefully monitors user attention and while publishers’ content is displayed. An award mechanism attributes a certain score to the publisher based on the attention paid by users and a number of BATs is calculated accordingly. At this point, the payment from the advertiser is performed on Brave Ledger System, to identify user preferences and to execute all transactions between the advertiser and the publisher without revealing users’ identities to the network. Looking at BAT’s functions and working flow, an open blockchain system is desirable both for security and scalability reasons. The fact that more and more developers are needed to enhance the Brave platform implies that new individuals need to constantly join the network. In this way, more peers will be incentivized to verify blocks of transactions on Ethereum’s blockchain and developers will be rewarded for their work. If BAT wasn’t built on top of an open Blockchain, BAT would be less scalable, and its utility would be limited.


3.3 Competition


In terms of the token’s performance within the Distributed Ledger Technology (DLT) market, BAT is among the most promising inventions. BAT is more advanced than its competitors in terms of both less centralization and market adoption. Remember that centralization is the main issue within the digital advertising market. In the BAT use case, the tendency to centralization compromises not only the underlying blockchain infrastructure but also BAT’s main scope: making the market distributed as opposed to concentrated. As for 2019, most Ethereum nodes on which BAT relies are located in the USA, China, and Canada, which comprise roughly 77% of the total network. Furthermore, it can be noted that there are some mining pools that are responsible for the majority of transactions mined. As is the case with all permissionless blockchain ecosystems, the fact that mining is geographically concentrated among a small number of mining pools can be a risk for therespective blockchain algorithm. BAT’s major mining pools are Ethermine, Snarkpool, Nanopool, f2pool2, and miningpoolhub1. Together, these mining pools are responsible for around 78% of the total hashing power of the network (Figure2.C).


The above graph only apparently shows an issue concerning BAT token. In fact, the problem of concentration relies upon Ethereum’s blockchain. As previously illustrated, BAT is built on top of the Ethereum blockchain; therefore, each BAT transaction is verified and executed by Ether mining pools. This fact also exposes BAT to another issue: network capacity. If wethink that BAT network’s performances are inevitably affected by Ethereum’s protocol, it is easy to understand that more dApps built on top of Ethereum’s blockchain will overload the computational power available for the entire decentralized ecosystem. In addition to computing power, concentration is manifested also in BAT’s network considered as independent from Ethereum’s protocol. Per Etherscan.io (Figure 2.D), until 2019 the top ten wallets possessed an aggregated value of 49.6% of the total amount of BATs in the network. As for August 2020, that figure has become less dramatic thanks to the increased use and demand of the token; the top ten wallets now cumulatively possess roughly 32% of the total amount of BATs.


BAT’s main competitors are: • AdEx (ADX) – another Ethereum-based project focused on digital advertising. The main difference between the two tokens is that BAT’s scope includes the entire digital advertising world, while AdEx is focused on digital video advertising. For this reason, users have less incentive to participate in a limited network such as AdEx.

DATA – this project is based on Tendermint rather than Ethereum protocol. Tendermint can be considered a more advanced technology than Ethereum because it avoids the issue of limited computational capacity (thanks to the Cosmos network). This implies that transactions take much less time to be verified and executed. Nonetheless, BAT is more active and serves many more users than DATA. As we have seen with the “traditional” digital advertising market, first movers have a great advantage over second entries. • Steem (STEEM) – Steem is a standalone blockchain protocol that exploits a Proof of Stake consensus algorithm in 2016 to give value to user contributions to build an efficient digital media platform. Every user is rewarded with a certain amount of STEEMs, which is the unit of account and which can be compared to BAT. In the case of STEEM, the reward depends on the number of votes that publishers receive from other peers. BAT and STEEM technologies could cooperate and therefore there is less competition between them in comparison with other tokens • AdToken (ADT) – The company that supports AdToken, another Ethereum-based crypto-asset, is adChain. The company’s scope is to create a list of clean publisher domains for advertisers who want their content to be published on non-fraudulent websites. Advertisers can identify publishers on adChain registry by an ID that indicates if the publisher is safe. In the case the publisher targeted by the advertiser is not in the adChain registry, that would mean the publisher has not been authenticated by adChain and therefore is not safe. AdToken holders vote on whether they think if a website is fraudulent or not. Based on the vote result, a publisher might be either added to the adChain registry or removed. This type of mechanism is in substance a proof of reputation, compared to BAT proof of attention. In this way, adChain can prevent domain spoofing, which is the main cause of the enormous amount of fake news present in the market. On the other hand, as I will explain shortly, the BAT addresses the issue of snooping. Both snooping and domain spoofing are among the main reasons why the current digital advertising market is overloaded by useless data. As they address two complementary issues, some developers think that the two tokens may cooperate rather than be compete.


According to the data furnished by Ethers.io, the BAT is less concentrated than its competitors. For instance, AdToken’s top two wallets already have around 40% of the total amount of tokens in the network. More importantly, as already mentioned, Brave is the most adopted platform so far. At the end of 2019, Brave's team launched the Brave 1.0 version, which led to an increase of 27% in monthly active user and on the 1st of June 2020, Brave has passed 15 million monthly active users. Moreover, as of today, there are more than 61,000 publishers in Brave network, up from 28,000 in 2018. Brave is also involving more and more partners within its network and that contributes to increasing its utility value. Some of BAT partners are:

  • Uphold - an application that allows converting BAT rewards in FIAT currency and other cryptocurrencies

  • YouTube - YouTube allows publishers and other content creators to be paid in BATs

  • DuckDuckGo - this is a search engine that does not collect user information and it can be selected as the default option to navigate the internet

  • MetaMask - Metamask is an Ethereum hot wallet that allows users to connect to the Ethereum blockchain and to store tokens such as BAT

  • Travala – known as the future leading blockchain-based travel booking platform, Travala offers already more than 2 million hotels and accommodations in 230 countries. The platform exploits AVA (Binance- Chain based token) and has recently launched a partnership with Brave for a global ad campaign

These companies help Brave to increase BAT adoption and to boost its utility in the economy. Overall, from a fundamental perspective, BAT works on a far more developed infrastructure than its main competitors.


3.4 Comparative Analysis


Furthermore, by exploiting blockchain technology and blocking external trackers, Brave software provides a higher level of security among users. In November 2019, after the launch of Brave 1.0, the company sent an email to the US Senate in an attempt to warn that conventional web browsers allow foreign state actors to analyse and execute code on US government computers by using targeted online ads. The warning has been made by a letter that comprehends a comparative table of the security protections of web browsers (Table 1.A).


As it is shown in the above table, Brave blocks almost all third-party trackers by default. The platform shields user privacy at various levels thanks to Brave Shields. This Brave extension is automatically activated, but you can always deactivate it from the address bar. For instance, Brave automatically upgrades connections to prevent snooping. Snooping is defined as the process of listening to Internet Protocol (IP) conversations between the routers and the host. Hypertext Transfer Protocol Secure (HTTPS) is an extension of HTTP that usually protects users from third-party listeners while they are filling any web page with confidential data such as passwords or personal information. In contrast with browsers such as Chrome, Safari and Firefox, Brave provides automatically upgraded connections to HTTPS. This is fundamental to prevent snooping and other forms of tracking because many websites support HTTPS but do not use it by default. Another important feature of Brave Shields is social media blocking. It is common to navigate on a particular social media platform and be subject to retargeted ads while viewing another website. This can happen because third-party cookies can track users for more time than just during the visit to a webpage. Brave Shields allows users to block banners and ads displayed from these types of cookies. Last but not least, people using Brave browser can opt to navigate in a private window through Tor anonymity. Tor24 prevents other websites from identifying users’ IP address. By contrast, most browsers just avoid creating browsing history on the device used to navigate on the internet. In this way, when one lands back on the website that he or she has previously visited under Tor anonymous connection, that website cannot recognize that he or she is the same person. As an effect, Brave not only guarantees better confidentiality in its network but also offers a better user experience. By shielding users from unwanted material, Brave loads websites up to 3 to 6 times faster than other browsers and introduces significant memory and battery savings on both desktop and mobile. Brave saves an average of about 27 seconds per page load against Chrome .


Conclusion


As explained so far, Brave Software has completely rethought the structure of the digital advertising market, enabling users to choose over which type of ads to view, without being tracked by external entities. In the following table, I resume the main differences between the status quo and the framework that Brave is creating.


It is instantly noticeable how BAT’s privacy-oriented framework would inevitably threaten the role of most of the intermediaries that contribute to the fragmentation of the current digital media market. Under Brave’s application of blockchain technology, the third alternative discussed in paragraph 1.5 and introduced by the Coase Theorem is eventually realized. Users are given their rights to navigate keeping their anonymity and choosing which content to visualize. As resumed by the above table, this new and distributed model allows for considerable increases in browser performance and security, from several points of view. If Brave were to be adopted by most users, the outcome would be a more competitive market and our governments would be less concerned about social media spending. Companies like Google and Facebook would have fewer reasons for lobbying, and they would have less room to influence people lives. Despite the concrete advantages that such a shift would lead to, it is important not to be over-optimistic. Brave still have many challenges to overcome before becoming a mature technology. Even though its user base is increasing at an incredible pace on a year over year basis, the great majority of people use more traditional browsers such as Google Chrome,Firefox, and Safari. Another obstacle is BAT’s centralization, which is decreasing in comparison with its competitors but still slightly present. Moreover, other protocols could lead to an even more efficient digital advertising market, as we have seen with Tendermint. It is also true that Brave provides a fertile ground for other actors to benefit from participation in its network. The first example is publishers, who are genuinely remunerated for their publications. Also, other blockchain applications can be considered complementary to BAT, like adChain, for instance. Finally, there are other entities like DuckDuckGo, Uphold and YouTube that, prioritising privacy, could highly benefit from introducing BAT in their interfaces. Taking all into consideration, BAT can be considered an extremely promising innovation that has the potential to efficiently re-allocate property rights in the digital advertising market and to contribute to flattening the structure of our society through blockchain technology.




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