CryptoPunks launched as a fixed set of 10,000 items in mid-2017 and became one of the inspirations for the ERC-721 standard. They have been featured in places like The New York Times, Christie’s of London, Art|Basel Miami, and The PBS NewsHour.
What is a CryptoPunk? The CryptoPunks are 24x24 pixel art images, generated algorithmically. Most are punky-looking guys and girls, but there are a few rarer types mixed in: Apes, Zombies and even the odd Alien. Every punk has their own profile page that shows their attributes as well as their ownership/for-sale status (here's an example).
What exactly is going on here? Cryptocurrency was made famous by Bitcoin, but Bitcoin is designed just to transact and store ownership of Bitcoin itself. We are using a successor to Bitcoin called Ethereum which allows for arbitrary computer code to be executed on the blockchain and the results of the execution to be stored forever. This is pretty cool! Normally code is run on a server somewhere and you basically need to trust the person running the server. Ethereum lets everyone execute the code, show each other what result they got, and agree that the code was executed properly and fairly.
We have written code that lives on the blockchain that anyone can use to buy and sell Punks with anyone else in the world. An interesting aspect of this system is that we no longer have any control over the code running CryptoPunks! Once we released it onto the blockchain it became permanently embedded there and can no longer be modified by anyone. This is scary for us as developers because we worry about bugs, but it is also a very powerful feature of the system. It allows a user verify that there are indeed only 10,000 punks, check that we can't steal them from you, and basically make sure that everything we told you about the code is true.
1.0 First-Mover Advantages
A first-mover advantage can be simply defined as a firm's ability to be better off than its competitors as a result of being first to market in a new product category.
First Mover Advantage (FMA) is one of most appealing and diffused concepts in strategy research; although a wide academic literature has flourished on this issue in the last twenty years, there is still no conclusive empirical evidence either in favor or against it. A natural, obvious candidate to put on trial for this unpleasant situation is the empirical literature: Lieberman and Montgomery (1988) recognized its lack of precision as a problem, and VanderWerf and Mahon (1997) showed that many issues that plagued the field (including the endogeneity of first-mover opportunities, the sample selection bias, and measurement problems) had a strong impact on the findings. On the other hand, many of these problems have been solved over time through the help of methodological advances and more suitable datasets (Bijwaard, Janssen, and Maasland, 2008; Eggers, Grajek, and Kretschmer, 2011). Moreover, some of the findings are at odds with theory-driven expectations: Makadok (1998) found evidence of FMA in a context where the ex-ante probability of observing it was very low and Lieberman (2007) showed that in the growing Internet industry the expectations about FMA, although high and widespread, were quite exaggerated. Therefore, the theoretical literature shares at least in equal measure the responsibility of the current situation. Suarez and Lanzolla (2007) concisely and sternly point out the inability of FMA theory to explain the conflicting empirical evidence and to provide coherent guidelines for managers. In this paper, we present a formal model of industry evolution that builds on the framework developed by Suarez and Lanzolla (2007) and derives implications regarding the effects of environmental influences – analyzed through the concepts of technological and demand regimes – on the emergence of first-mover advantages. The model is rooted in the evolutionary economics tradition (Nelson and Winter, 1982) and exploits the experience of the “history-friendly models” in reducing the gap between the richness and complexity of the empirical evidence and the abstractness of formal models. It draws its building blocks from “history-friendly” models describing the actual evolution of the computer (Malerba, Nelson, Orsenigo and Winter, 1999) and 4 the pharmaceutical industry (Malerba and Orsenigo, 2002), and combines them in order to represent many different technology and market contexts and allow comparative exercises.
Crypto Punks is one of the leading NFT Projects thanks to its first-mover advantages. First-mover advantages benefit from a number of advantages the late movers do not possess.
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