Cryptocurrencies network effects and switching costs barrier
Web3 has become the defining technology trend of — and network effects are at the heart of it. First do the background settings of Web The purpose of this post is not to unravel the possibilities of web3 or the complexities of the technology, but to focus on the nature of network effects in this era. These web3 models share several characteristics:. I will explain these patterns with the help of two case studies — Ethereum and Axie Infinity, one of the most successful web3 projects to date. They are also representative examples because their network effects share many characteristics with the wider web3 field.
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Content:
- Exploring the Disruptiveness of Cryptocurrencies: A Causal Layered Analysis-Based Approach
- Network Effect: A Proven Way to Create a Moat
- Introduction to Digital Economics
- The Cost of Bitcoin Mining Has Never Really Increased
- Raoul Pal: The Inconvenient Truth About Crypto
- Network effect
- DISH, Overstock, and Bootstrapping a New Currency
Exploring the Disruptiveness of Cryptocurrencies: A Causal Layered Analysis-Based Approach
Bitcoin provides its users with transaction-processing services which are similar to those of traditional payment systems. We find that this decentralized design protects users from monopoly pricing. Competition among service providers within the platform and free entry imply no entity can profitably affect the level of fees paid by users.
Instead, a market for transaction-processing determines the fees users pay to gain priority and avoid transaction-processing delays. The Appendix describes and explains the main attributes of Bitcoin and the underlying blockchain technology. The recipients of this revenue—payment-processing firms—enjoy network effects and economies of scale, and therefore limited competition and barriers to entry Rosenbaum et al. Multiple lawsuits against payment-processing firms accuse them of abusing their market power and harming welfare.
The Bitcoin Payment System BPS , a platform that provides payment services, shows the feasibility of an alternative, decentralized design. It has been operating reliably since its early inception. Even a miner who controls a large fraction of the computational power cannot profitably affect fees. Moreover, the fees users pay do not increase if users lose their alternative payment methods.
Standard economic arguments suggest that weak competition among monopolistic firms calls for regulation to mitigate monopoly harm. Under the BPS, users are protected from abuses of monopoly power even without competition from other payment systems.
Thus, the BPS addresses potential antitrust concerns in a novel, even revolutionary, way. In the absence of a price-setting firm, the BPS relies on a market mechanism encoded in its protocol to determine prices and infrastructure. Our analysis of the protocol reveals inefficiencies in this market mechanism. Among them is the lack of a mechanism that drives the level of resources acquired and deployed to an efficient level, however defined. We provide design suggestions to address these concerns.
A brief description of the system is in order to explain the particular properties of this two-sided market that are the focus of our model. For concreteness, we focus on the BPS, whose basic design features are shared among most other cryptocurrencies. Appendix A provides a more detailed description of the BPS which is targeted for economists.
Users post transactions over time; miners organize them into blocks, each block with the same, limited capacity; the block of a single randomly selected miner is added to the blockchain; this block selection amounts to processing of the transactions in that block; the timing of miner selection is a Poisson process with a fixed rate which is independent of the aggregate computing resources used by the miners.
To make the presentation cleaner, we assume, that on average, the system has sufficient capacity to process all transactions. All miners perform the same tasks. Participation in the miner selection tournament is the most resource-consuming among these.
The selected miner is said to have mined a block, and is rewarded with a fixed, system-generated reward plus the fees associated with the transactions in that block. Each user chooses the fee associated with his transaction.
Each miner is free to enter and exit the system at no cost. Each participating miner chooses which transactions to include in his block. We set up a model of fees, priority levels, and mining intensity that captures the main features of the BPS.
Its analysis highlights differences between the BPS and a traditional payment system operated by a profit-maximizing firm. The analysis delivers explicit formulas of the fees and delays, thereby enabling suggestions for design improvements.
Figure 1 suggests an agreement between the fee formula and the data. Actual and model predicted transaction fees per block in USD and block size for the Bitcoin Payment System daily averages, 1 April — 30 June The chart on the left shows fees on a linear scale from the entire range of dates; the chart on the right shows fees on a logarithmic scale over periods when block size was above 0. See Section 6. Beyond the quantitative results, the analysis offers a series of qualitative insights as follows.
The BPS processes all transactions, albeit with delay; all users receive strict positive surplus. In contrast, in our setting a profit-maximizing firm excludes low willingness to pay WTP transactions but processes the rest without delay. User payments under the BPS are determined by a congestion market and are payments for service speed. A profit-seeking miner excludes the transactions which offer the lowest fees when the assembled block is full.
Therefore, users to whom delays are costly will offer relatively high fees to gain priority and be served faster. In equilibrium, users with higher delay costs receive higher processing priority and therefore shorter delays. The fee a user pays is equal to the expected delay externality he imposes on others who offer lower fees. This implies users are protected from price increase should alternative payment service providers leave the market.
An increase respectively, decrease in the arrival rate of new transactions results in increased respectively decreased congestion, which in turn causes fees to be higher respectively lower. No delays imply no fees. The analysis offers an explicit relation between block size which reflects congestion and the USD-denominated fee. Figure 1 provides a theoretical and an empirical summary of this relation. We show that even a miner who controls a substantial fraction of the mining resources cannot profitably affect the fees paid by users.
In contrast to standard platform competition, new miners face no barriers to entry as they enter and compete within the same platform.
Free entry of miners is essential to this result. Newly minted coins and transaction fees fund the miners who acquire mining resources in USD-denominated markets. There is no mechanism that drives the level of infrastructure resources acquired and deployed to an efficient level, however defined.
The analysis points to an efficiency contrast between the BPS and a profit-maximizing firm. If miners are homogeneous, all surplus accrues to the users. However, the costs of operating the BPS are likely to be higher than those of a traditional firm: its decentralized architecture requires duplication of computations and expenditure of efforts in the miner selection tournament; the aggregate mining level can be too high; costly delays are necessary to induce users to pay transaction fees.
Thus, welfare under the BPS can be higher or lower than that under a traditional system, depending on the value of eliminating monopoly dead-weight loss.
Hundreds of variants of Bitcoin have emerged, with many aiming to improve on the original Nakamoto design. Our analysis provides the following messages to designers. First, it suggests that congestion is not merely an engineering necessity but also a device to motivate users to pay transaction fees.
Second, the analysis suggests a simple modification that avoids the variation in revenue from transaction fees. In the BPS, capacity is fixed and congestion varies with demand; consequently, the revenue and infrastructure levels vary over time.
This design has two advantages over alternatives such as a fixed transaction fee: 1 it allows the system to raise revenue without excluding transactions, as users can choose to pay no fees but incur delays; 2 it allows the protocol to obtain the USD market value of delay reduction without the need to learn the exchange rate.
The analysis also allows us to optimize parameter choices. We offer an analytic expression for the delay costs required to raise a certain revenue level.
Analysis and examples suggest that large blocks are less efficient in that they require longer delays to sustain a given level of revenue. The cost of mediation increases transaction costs, limiting the minimum practical transaction size and cutting off the possibility for small casual transactions.
Kroll et al. We believe that a rules change would be necessary before transactions fees can play any major role in the Bitcoin economy. Following the initial version of this article, the design of transaction fee mechanisms has received attention from both academics and practitioners e. Buterin, Easley et al. The data appear consistent with these predictions.
Lavi et al. Prat and Walter study the dynamics of miner entry as it is influenced by changes in exchange rates and technological changes and predictions thereof.
Felten argues that in equilibrium miners break even. Cong et al. Arnosti and Weinberg develop a model where miners are heterogeneous in their cost structure, and quantifies how such asymmetries lead to the formation of oligopolies and concentration of mining power. Eyal and Sirer and Sapirshtein et al. Babaioff et al. Leshno and Strack and Chen et al. Narayanan et al.
Croman et al. Eyal et al. Carlsten et al. Chiu and Koeppl evaluate the welfare implications of printing new coins. The protocol proposed by Nakamoto posits that in case of a fork, miners will follow the longest branch. Biais et al. Abadi and Brunnermeier posit three desired properties of distributed ledger technologies, 1 correctness, 2 decentralization, and 3 cost efficiency and argue that no ledger can satisfy all three properties simultaneously.
Instead bitcoin resembles a speculative investment similar to the Internet stocks of the late s. Gandal and Halaburda analyse competition between the different cryptocurrencies. Halaburda and Sarvary review the cryptocurrency market, its development, and future potential of blockchain technology. Gans and Halaburda analyse the economics of digital currencies, focusing on platform-sponsored credits. Catalini and Gans discuss possible opportunities that can arise from blockchain technology.
Huberman et al. Recent work considers the valuation of bitcoin relative to fiat currencies and other goods. That work usually assumes away the limited capacity of the BPS, although it induces delays and transaction fees. Ron and Shamir and Athey et al.
Network Effect: A Proven Way to Create a Moat
A business moat is a key competitive advantage that sets a company apart from its competitors. From Amazon and Tesla to Starbucks and Coinbase, here is how 25 of the world's biggest companies have built and defended their moats. What do companies like Amazon , Uber , and Starbucks have in common? Among several shared characteristics, these companies thrive by understanding, building, and strengthening their business moats — the key competitive advantages that set them apart. Companies can build moats by strengthening their brands, achieving economies of scale, or even lobbying for special status from the government. In return, they can receive customer loyalty, pricing power, and legal protections that make it difficult for other companies to compete with them.
Introduction to Digital Economics
In economics , a network effect also called network externality or demand-side economies of scale is the phenomenon by which the value or utility a user derives from a good or service depends on the number of users of compatible products. Network effects are typically positive, resulting in a given user deriving more value from a product as other users join the same network. The adoption of a product by an additional user can be broken into two effects: an increase in the value to all other users "total effect" and also the enhancement of other non-users' motivation for using the product "marginal effect". Network effects can be direct or indirect. Direct network effects arise when a given user's utility increases with the number of other users of the same product or technology, meaning that adoption of a product by different users is complementary. Network effects are commonly mistaken for economies of scale , which describe decreasing average production costs in relation to the total volume of units produced. Economies of scale are a common phenomenon in traditional industries such as manufacturing, whereas network effects are most prevalent in new economy industries, particularly information and communication technologies.
The Cost of Bitcoin Mining Has Never Really Increased
Bitcoin provides its users with transaction-processing services which are similar to those of traditional payment systems. We find that this decentralized design protects users from monopoly pricing. Competition among service providers within the platform and free entry imply no entity can profitably affect the level of fees paid by users. Instead, a market for transaction-processing determines the fees users pay to gain priority and avoid transaction-processing delays. The Appendix describes and explains the main attributes of Bitcoin and the underlying blockchain technology.
Raoul Pal: The Inconvenient Truth About Crypto
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Network effect
The network effect is a phenomenon whereby increased numbers of people or participants improve the value of a good or service. The Internet is an example of the network effect. Initially, there were few users on the Internet since it was of little value to anyone outside of the military and some research scientists. However, as more users gained access to the Internet, they produced more content, information, and services. The development and improvement of websites attracted more users to connect and do business with each other. As the Internet experienced increases in traffic, it offered more value, leading to a network effect.
DISH, Overstock, and Bootstrapping a New Currency
Author: Contributor Date: June 4, Currently, over 22, businesses worldwide accept Bitcoin as payment, including major companies such as PayPal, Home Depot, and Starbucks. However, network effects can also inhibit growth and yield negative results. Read on to learn what network effects are, how crypto plays into them, and whether its success depends on them.
When network products and services become more valuable as their userbase grows network effects , this tendency can become a major determinant of how they compete with each other in the market and how the market is structured. Network effects are traditionally linked to high market concentration, early-mover advantages, and entry barriers, and in the market they have also been used as a valuation tool. The recent resurgence of Bitcoin has been partly attributed to network effects, too. We study the existence of network effects in six cryptocurrencies from their inception to obtain a high-level overview of the application of network effects in the cryptocurrency market. We show that, contrary to the usual implications of network effects, they do not serve to concentrate the cryptocurrency market, nor do they accord any one cryptocurrency a definitive competitive advantage, nor are they consistent enough to be reliable valuation tools. Therefore, while network effects do occur in cryptocurrency networks, they are not yet a defining feature of the cryptocurrency market as a whole.
Why do digital industries routinely lead to one company having a very large share of the market at least if one defines markets narrowly? To anyone familiar with competition policy discussions, the answer might seem obvious: network effects, scale-related economies, and other barriers to entry lead to winner-take-all dynamics in platform industries. Accordingly, it is that believed the first platform to successfully unlock a given online market enjoys a determining first-mover advantage. This narrative has become ubiquitous in policymaking circles. But are network effects and the like the only ways to explain why these markets look like this? While there is no definitive answer, scholars routinely overlook an alternative explanation that tends to undercut the narrative that tech markets have become non-contestable.
This moat investing education series explores the five primary sources of moat, according to Morningstar: 1 switching costs ; 2 intangible assets ; 3 network effect; 4 cost advantage ; 5 efficient scale. Here we explore the concept of network effect. It describes the phenomenon where the value of a product or service increases as the number of its users grows.
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