Why airdrop cryptocurrency tokens?

Abstract: A cryptocurrency token airdrop is a novel means of distributing rights over a blockchain project to a community of users and owners for free. The market value of these airdrop giveaways is often upwards of hundreds of millions of dollars. This paper considers why projects might choose this unusual and costly means of token distribution. It considers a selection of high-profile airdrops as case studies between 2014 and 2022. This is the first comprehensive analysis of the rationales and mechanisms of Web3 token airdrops. We find that two primary rationales for airdrops are marketing (to attract new users and to maintain a community) and decentralisation of ownership and control of a project (building community, providing regulatory protection, and enhancing security). The paper contributes to an understanding of business practice and strategy in the emerging cryptocurrency and blockchain industry.

Author(s): Darcy W. E. Allen, Chris Berg, Aaron M. Lane

Journal: Journal of Business Research

Vol: 163 Year: 2023 Article: 113945

DOI: 10.1016/j.jbusres.2023.113945 and manuscript version at SSRN

Cite: Allen, Darcy W. E., Chris Berg, and Aaron M. Lane. “Why Airdrop Cryptocurrency Tokens?” Journal of Business Research, vol. 163, 2023, article 113945.

Continue reading “Why airdrop cryptocurrency tokens?”

The Case for Generative AI in Scholarly Practice

Available at SSRN

Abstract: This paper defends the use of generative artificial intelligence (AI) in scholarship and argues for its legitimacy as a valuable tool for contemporary research practice. It uses a emergent property rights model of writing to shed light on the evolution of scholarly norms and practices in academic practice. The paper argues that generative AI extends the capital-intensive nature of modern academic writing. The paper discussing three potential uses for AI models in research practice: AI as a mentor, AI as an analytic tool, and AI as a writing tool. The paper considers how the use of generative AI interacts with two critical norms in scholarship: norms around authorship attribution and credits for contributions, and the norm against plagiarism. It concludes that the effective use of generative AI is a legitimate research practice for scholars seeking to experiment with new technologies that might enhance their productivity.

Review: Voting over a Distributed Ledger: An Interdisciplinary Perspective

Published in Journal of Economic Literature, 61 (1): 295–97. Available at the Journal of Economic Literature

A review of: Voting over a Distributed Ledger: An Interdisciplinary Perspective. By Amrita Dhillon, Grammateia Kotsialou, Peter McBurney, and Luke Riley. Foundations and Trends in Microeconomics, vol. 12, no. 3. Boston: Now, 2021.

Fraud is a stubborn problem in democratic elections, and the history of voting technology is a history of attempts to reduce it. Votes might be manipulated at the ballot box, while voting records are in transit to counting centers, at the counting centers themselves, and at the point of broadcasting the result of an election. As Jones (2006) shows, many political reformers of the nineteenth century were technologists who designed voting machines that they believed would not just make elections more efficient, but also reduce real and perceived threats to the legitimacy of election results.

In their monograph, Voting over a Distributed Ledger: An Interdisciplinary Perspective, Amrita Dhillon, Grammateia Kotsialou, Peter McBurney, and Luke Riley explore and propose a significant upgrade to the technical infrastructure of voting: electronic voting over the internet using blockchain technology. Blockchains, the distributed ledger technology that underpins cryptocurrencies such as bitcoin and ethereum, use a combination of cryptography and economic incentives to achieve consensus over the state of a shared ledger without the need for a central authority to maintain and enforce that consensus. Dhillon et al. set up the terms of their investigation as follows. There are many intuitively appealing characteristics of voting over the internet. Voters could vote from their home devices or on mobile phones, potentially enhancing voter turnout (particularly among those who, for disability or socioeconomic reasons, find it difficult to turn up to a ballot box). Fully electronic votes could be counted immediately and at far lower cost.

Yet, as the authors detail in chapter 2, online voting has a wide range of risks that can threaten the real or perceived legitimacy of an election’s results. Some of these are cybersecurity risks: the software on a voters’ device might be corrupted through malware that manipulates the vote as it is cast, or the infrastructure that communicates votes to a counting authority could be targeted by adversaries with a denial-of-service attack. A counting service could go offline, possibly causing confusion that would lead to claims about illegitimacy. A more severe potential problem is that the counting authority might itself be corrupt. It is often observed that algorithms can be a black box to all but their creators; the difficulty of auditing a counting system might allow fraud to pass undetected. These potential risks are why many jurisdictions still use paper ballots or machines that print paper receipts-each of which present an audit trail manual verification by election supervisors or the voters themselves.

In chapters 3 and 4 the authors provide a straightforward description first of distributed ledger technology as a general category of decentralized systems, and then of blockchains as a special case of decentralized ledger. They distinguish correctly between “permissionless” systems, such as Bitcoin, where any user can join and participate in the consensus mechanism, and “permissioned” systems that will typically have a closed (and known) set of validators.

Chapters 5 and 6 are the core of the monograph, where the authors describe a high-level design of a blockchain voting system. Voters would be given a unique identity and vote by submitting a transaction directly to a node on the network. The node would then propagate that vote across the network. A voter could verify, using their private keys, that their vote had been recorded correctly and was included in the total vote count. Distributing control of the voting system across multiple nodes would reduce the risk of corruption by a central authority as well as provide a path for external observers to validate the correctness of the final count. Vote secrecy and anonymity could be assured using frontier cryptographic techniques such as zero-knowledge proofs that would allow anyone to check if a vote is valid without revealing any information about the vote other than its validity.

Voting over a Distributed Ledger is an effective argument for the feasibility of blockchain voting, but it is surprisingly modest. Some short sections suggest a much more ambitious vision. As the authors note, the step change in efficiency and reliability offered by blockchains could allow for the adoption of alternative voting schemes. Better technologies for voting open up possibilities for innovation in democratic form. It is hard, for example, to imagine a quadratic voting scheme (Weyl 2017) being built on paper ballots alone. In work with colleagues (Allen, Berg, and Lane 2019), I have explored a form of blockchain-based “liquid democracy” where voters could delegate their voting power while at the same time reserving the ability to directly vote on more personally salient issues.

Indeed, we already can see some of the possibilities of democratic innovation in the way that blockchain-based communities have innovated on the voting systems they use for organizational governance. Delegative democracy where voters retain an option to override their delegate’s vote is a core part of the Cosmos SDK, one of the major classes of blockchain protocols. More sophisticated systems are widespread in decentralized autonomous organizations (DAOs). One compelling example is the time-weighted voting systems that grant DAO token holders additional voting power for having locked up their tokens for a period of time, used most famously by the Curve protocol. As these examples show, reducing fraud, lowering costs, and increasing the speed with which elections are counted are important goals, but blockchains offer something else that should excite social scientists: a vast new design space for building systems of collective decision-making.

REFERENCES

Allen, Darcy W. E., Chris Berg, and Aaron M. Lane. 2019. Cryptodemocracy: How Blockchain Can Radically Expand Democratic Choice. Lanham, MD: Lexington Books.

Jones, Douglas W. 2006. “Technologists as Political Reformers: Lessons from the Early History of Voting Machines.” Paper presented at the Society for the History of Technology Annual Meeting, Las Vegas, NV, October 13. https://homepage.divns.uiowa.edu/ jones/voting/SHOTpaper.pdf.

Weyl, E. Glen. 2017. “The Robustness of Quadratic Voting.” Public Choice 172 (1-2): 75-107.

How Web3’s ‘programmable commerce layer’ will transform the global economy

World Economic Forum, 28 November 2022. Originally published here. With Justin Banon, Jason Potts and Sinclair Davidson.

The world economy is in the early stages of a profound transition from an industrial to a digital economy.

The industrial revolution began in a seemingly unpromising corner of northwest Europe in the early 1800s. It substituted machine power for animal and human power, organized around the factory system of economic production. Soon, it created the conditions to lift millions of humans from a subsistence economy into a world of abundance.

The digital economy began with similarly unpromising origins when Satoshi Nakomoto published his Bitcoin white paper to an obscure corner of the internet in late 2008. We call this the origin of Web3 now – with the first blockchain – but this revolution traces back decades as the slow economic application of scientific and military technologies of digital communication. The first wave of innovation was in computers, cryptography and inter-networking – Web1.

By the late 1990s, so-called “e-commerce” emerged as new companies, which soon became global platforms, built technologies that enabled people to find products, services and each other through new digital markets. That was Web2, the dot-com age of social media and tech giants.

But the actual age of digital economies was not down to these advances in information and communications technologies but to a very different type of innovation: the manufacture of trust. And blockchains industrialize trust.

Industrial economies industrialized economic production using physical innovations, such as steam engines and factories. Such institutional technologies organize people and machines into high production. What the steam engine did for industry, the trust engine will do for society. The fundamental factor of production that a digital economy economizes on is trust.

Blockchain is not a new tool. It is a new economic infrastructure that enables anyone, anywhere, to trust the underlying facts recorded in a blockchain, including identity, ownership and promises represented in smart contracts.

These economic facts are the base layer of any economy. They generally work well in small groups – a family, village or small firm – but the verification of these facts and monitoring of how they change becomes increasingly costly as economic activity scales up.

Layers of institutional solutions to trust problems have evolved over perhaps thousands of years. These are deep institutional layers – the rule of law, principles of democratic governance, independence of bureaucracy etc. Next, there are administrative layers containing organizational structures – the public corporation, non-profits, NGOs and similar technologies of cooperation. Then we have markets – institutions that facilitate exchange between humans.

It has been the ability to “truck, barter and exchange” over increasing larger markets that has catapulted prosperity to the levels now seen around the world.

Information technology augments our ability to interact with other people at all levels – economic, social and political. It has expanded our horizons. In the mid-1990s, retail went onto the internet. The late 1990s saw advertising on the internet. While the mid-2000s saw the news, information and friendship groups migrate to the internet. Since their advent in 2008, cryptocurrencies and natively digital financial assets have also come onto the internet. The last remaining challenge is to put real-world (physical) assets onto the internet.

The technology to do so already exists. Too many people think of non-fungible tokens (NFTs) as trivial JPEGs. But NFTs are not just collectable artworks; they are an ongoing experiment in the evolution of digital property rights. They can represent a certificate of ownership or be a digital twin of a real-world asset. They enable unique capital assets to become “computable,” that is, searchable, auditable and verifiable. In other words, they can be transacted in a digital market environment with a low cost of trust.

The internet of things can track real-world assets in real-time. Oracles can update blockchains regarding the whereabouts of physical assets being traded on digital markets. For example, anyone who has used parcel tracking over the past two years has seen an early version of this technology at work.

Over the past few years, people have been hard at work building all that is necessary to replicate real-world social infrastructure in a digital world. We now have money (stablecoins), assets (cryptocurrencies e.g. Bitcoin), property rights (NFTs) and general-purpose organizational forms (decentralized autonomous organizations (DAOs)). Intelligent people are designing dispute-resolution mechanisms using smart contracts. Others are developing mechanisms to link the physical and digital worlds (more) closely.

When will all this happen? The first-mover disadvantage associated with technological adoption has been overcome, mostly by everyone having to adopt new practices and technology simultaneously. Working, shopping and even entertaining online is now a well-understood concept. Digital connectedness is already an integral part of our lives. A technology that enhances that connectedness will have no difficulty in being accepted by most users.

It is very easy to imagine an interconnected world where citizens, consumers, investors and workers seamlessly live their lives transitioning between physical and digital planes at will before the decade concludes.

Such an economy is usefully described as a digital economy because that is the main technological innovation. And the source of economic value created is rightly thought of as the industrialization of trust, which Web3 technologies bring. But when the physical parts of the economy and the digital parts become completely and seamlessly join, this might well be better described as a “computable economy.” A computable economy has low-cost trust operating at global market scale.

The last part of this system that needs to fall into place is “computable capital.”

Now that we can tokenize all the world’s physical products and services into a common, interoperable format; list them within a single, public ledger; and enable market transactions with low cost of trust, which are governed by rules encoded within and enforced by the underlying substrate, what then?

Then, computable capital enables “programmable commerce,” but more than that – it enables what we might call a “turing-complete economy.”

Repugnant innovation

Abstract: Repugnant innovation is a form of evasive entrepreneurship that occurs in repugnant markets. Repugnance is an informal institution – controlled by long-lived norms, attitudes, customs and traditions – and repugnant innovation acts to shift institutions at the lowest level of the institutional stack. The paper considers three examples of repugnant innovation: e-cigarettes, online gambling, and webcam modelling. Each repugnant innovation challenges the complex mixture of material and moral concerns that contributes to repugnance in their respective markets. The paper adds to and expands on a body of evidence about innovation in apparently unsupportive institutional environments.

Author(s): Darcy W. E. Allen, Chris Berg, Sinclair Davidson

Journal: Journal of Institutional Economics

Year: 2022 Pages: 1–12

DOI: 10.1017/S1744137422000364. Working paper at SSRN.

Cite: Allen, Darcy W. E., Chris Berg, and Sinclair Davidson. “Repugnant Innovation.” Journal of Institutional Economics, 2022, pp. 1–12.

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Buyback and Burn Mechanisms: Price Manipulation or Value Signalling?

With Darcy WE Allen and Sinclair Davidson. Available at SSRN

Abstract: A core finding in traditional corporate finance is that manipulating funding instruments does not increase the value of a firm. Several Web3 projects have mechanisms to buy their tokens on the market and burn those tokens. If the finding from corporate finance holds in the Web3 environment then this manipulation of the value of tokens should not increase the value of those projects. This paper asks if these mechanisms serve more of a purpose than price manipulation. We provide an efficiency explanation for buyback and burn mechanisms: value signalling. A buyback and burn enables projects to signal that their business model has genuine network effects, and that it is not a Ponzi scheme. This finding has implications for the motivation, justification and design of buyback and burn mechanisms across Web3.

Crypto-macroeconomics

With Jason Potts and Sinclair Davidson. Book chapter, available at SSRN.

Abstract: This chapter presents a Wagnerian vision of macroeconomics as a hybrid of several schools of thought and analytic frameworks, including public choice theory, constitutional economics, complexity economics, and evolutionary economics. We then review recent economic analysis of emerging crypto-economic systems. Toward synthesis, we propose that Wagnerian macroeconomics is a useful framework to understand how blockchains and crypto assets provide economic infrastructure and institutions for new private order economies, a new research field we call crypto-macroeconomics. We explore four proposed subfields of crypto-macroeconomics: technology, constitutions, money, and policy.

Interoperability as a critical design choice for central bank digital currencies


Working paper available at SSRN

Abstract: Interoperability is a key economic and technical consideration for payment systems. This paper explores the implications of interoperability for central bank digital currencies (CBDCs). CBDCs are digital representations of central bank money. A critical question is how those digital representations can interoperate with other CBDCs, private blockchains, and permissioned blockchains. By comparing prevailing CBDC interoperability models with interoperability in blockchain ecosystems, the paper finds that CBDC architectural choices are deeply intertwined with policy choices in a way not yet understood by the scholarly and policy literature. Widely discussed CBDC policy questions (such as whether a CBDC should be retail or wholesale, whether interest should be paid on CBDC holdings, and how privacy should be protected) are better understood as choices around interoperability. The paper concludes by connecting the CBDC policy debate to a parallel debate about fiat-backed stablecoin architecture and governance.

Why a US crypto crackdown threatens all digital commerce

Australian Financial Review, 10 August 2022

The US government’s action against the blockchain privacy protocol Tornado Cash is an epoch-defining moment, not only for cryptocurrency but for the digital economy.

On Tuesday, the US Treasury Department placed sanctions on Tornado Cash, accusing it of facilitating the laundering of cryptocurrency worth $US7 billion ($10.06 billion) since 2019. Some $455 million of that is connected to a North Korean state-sponsored hacking group.

Even before I explain what Tornado Cash does, let’s make it clear: this is an extraordinary move by the US government. Sanctions of this kind are usually put on people – dictators, drug lords, terrorists and the like – or specific things owned by those people. (The US Treasury also sanctioned a number of individual cryptocurrency accounts, in just the same way as they do with bank accounts.)

But Tornado Cash isn’t a person. It is a piece of open-source software. The US government is sanctioning a tool, an algorithm, and penalising anyone who uses it, regardless of what they are using it for.

Tornado Cash is a privacy application built on top of the ethereum blockchain. It is useful because ethereum transactions are public and transparent; any observer can trace funds through the network. Blockchain explorer websites such as Etherscan make this possible for amateur sleuths, but there are big “chain analysis” firms that work with law enforcement that can link users and transactions incredibly easily.

Tornado Cash severs these links. Users can send their cryptocurrency tokens to Tornado Cash, where they are mixed with the tokens of other Tornado Cash users and hidden behind a state-of-the-art encryption technique called “zero knowledge proofs”. The user can then withdraw their funds to a clean ethereum account that cannot be traced to their original account.

Obviously, as the US government argues, there are bad reasons that people might want to use such a service. But there are also very good reasons why cryptocurrency users might want to protect their financial privacy – commercial reasons, political reasons, personal security, or even medical reasons. One mundane reason that investment firms used Tornado Cash was to prevent observers from copying their trades. A more serious reason is personal security. Wealthy cryptocurrency users need to be able to obscure their token holdings from hackers and extortionists.

Tornado Cash is a tool that can make these otherwise transparent blockchains more secure and more usable. No permission has to be sought from anyone to use Tornado Cash. The Treasury department has accused Tornado Cash of “laundering” more than $US7 billion, but that seems to be the total amount of funds that have used the service at all, not the funds that are connected to unlawful activity. There is no reason to believe that the Tornado Cash developers or community solicited the business of money launderers or North Korean hackers.

Now American citizens are banned from interacting with this open-source software at all. It is a clear statement from the world’s biggest economy that online privacy tools – not just specific users of those tools, but the tools themselves – are the targets of the state.

We’ve been here before. Cryptography was once a state monopoly, the exclusive domain of spies, diplomats and code breakers. Governments were alarmed when academics and computer scientists started building cryptography for public use. Martin Hellman, one of those who invented public key cryptography in the 1970s (along with Whitfield Diffie and Ralph Merkle), was warned by friends in the intelligence community his life was in danger as a result of his invention. In the so-called “crypto wars” of the 1990s, the US government tried to enforce export controls on cryptographic algorithms.

One of the arguments made during those political contests was that code was speech; as software is just text and lines of code, it should be protected by the same constitutional protections as other speech.

GitHub is a global depository for open-source software owned by Microsoft. Almost immediately after the Treasury sanctions were introduced this week, GitHub closed the accounts of Tornado Cash developers. Not only did this remove the project’s source code from the internet, GitHub and Microsoft were implicitly abandoning the long-fought principle that code needs to be protected as a form of free expression.

An underappreciated fact about the crypto wars is that if the US government had been able to successfully restrict or suppress the use of high-quality encryption, then the subsequent two decades of global digital commerce could not have occurred. Internet services simply would not have been secure enough. People such as Hellman, Diffie and Merkle are now celebrated for making online shopping possible.

We cannot have secure commerce without the ability to hide information with cryptography. By treating privacy tools as if they are prohibited weapons, the US Treasury is threatening the next generation of commercial and financial digital innovation.

Reliable systems out of unreliable parts

Amsterdam Law & Technology Institute Forum, 27 July 2022. Originally published here.

How we understand where something comes from shapes where we take it, and I’m now convinced we’re thinking about the origins of blockchain wrong.

The typical introduction to blockchain and crypto for beginners – particularly non-technical beginners – gives Bitcoin a sort of immaculate conception. Satoshi Nakamoto suddenly appears with a fully formed protocol and disappears almost as suddenly. More sophisticated introductions will observe that Bitcoin is an assemblage of already-existing technologies and mechanics – peer to peer networking, public-key cryptography, the principle of database immutability, the hashcash proof of work mechanism, some hand-wavey notion of game theory – put together in a novel way. More sophisticated introductions again will walk through the excellent ‘Bitcoin’s academic pedigree’ paper by Arvind Narayanan and Jeremy Clark that guides readers through the scholarship that underpins those technologies.

This approach has many weaknesses. It makes it hard to explain proof-of-stake systems, for one. But what it really misses – what we fail to pass on to students and users of blockchain technology – is the sense of blockchain as a technology for social systems and economic coordination. Instead, it comes across much more like an example of clever engineering that gave us magic internet money. We cannot expect every new entrant or observer of the industry to be fully signed up to the vision of those that came before them. But it is our responsibility to explain that vision better.

Blockchains and crypto are the heirs of a long intellectual tradition building fault tolerant distributed systems using economic incentives. The problem this tradition seeks to solve is: how can we create reliable systems out of unreliable parts? In that simply stated form, this question serves not just as a mission statement for distributed systems engineering but for all of social science. In economics, for example, Peter Boettke and Peter Leeson have called for a ‘robust political economy’, or the creation of a political-economic system robust to the problems of information and incentives. In blockchain we see computer engineering converge with the frontiers of political economy. Each field is built on radically different assumptions but have come to the same answers.

So how can we tell an alternative origin story that takes beginners where they need to go? I see at least two historical strands, each of which take us down key moments in the history of computing.

The first starts with the design of fault tolerant systems shortly after the Second World War. Once electronic components and computers began to be deployed in environments with high needs for reliability (say, for fly-by-wire aircraft or the Apollo program) researchers turned their mind to how to ensure the failure of parts of a machine did not lead to critical failure of the whole machine. The answer was instinctively obvious: add backups (that is, multiple redundant components) and have what John von Neumann in 1956 called a ‘restoring organ’ combine their multiple outputs into a single output that can be used for decision-making.

But this creates a whole new problem: how should the restoring organ reconcile those components’ data if they start to diverge from each other? How will the restoring organ know which component failed? One solution was to have the restoring organ treat each component’s output as a ‘vote’ about the true state of the world. Here, already, we can see the social science and computer science working in parallel: Duncan Black’s classic study of voting in democracies, The Theory of Committees and Elections was published just two years after von Neumann’s presentation of the restoring organ tallying up the votes of its constituents.

The restoring organ was a single, central entity that collated the votes and produced an answer. But in the distributed systems that started to dominate the research on fault tolerance through the 1970s and 1980s there could not be a single restoring organ – the system would have come to consensus as a whole. The famous 1982 paper ‘The Byzantine Generals’ Problem’ paper by Leslie Lamport, Robert Shostak and Marshall Peace (another of the half-taught and quarter-understood parts of the origins of blockchain canon) addresses this research agenda by asking how many voting components are needed for consensus in the presence of faulty – malicious – components. One of their insights was cryptographically unforgeable signatures makes the communication of information (‘orders’) much simplifies the problem.

The generation of byzantine fault tolerant distributed consensus algorithms that were built during the 1990s – most prominently Lamport’s Paxos and the later Raft – now underpin much of global internet and commerce infrastructure.

Satoshi’s innovation was to make the distributed agreement system permissionless – more precisely, to join the network as a message-passer or validator (miner) does not require the agreement of all other validators. To use the Byzantine generals’ metaphor, now anyone can become a general.

That permissionlessness gives it a resilience against attack that the byzantine fault tolerant systems of the 1990s and 2000s were never built for. Google’s distributed system is resilient against a natural disaster, but not a state attack that targets the permissioning system that Google as a corporate entity oversees. Modern proof-of-stake systems such as Tendermint and Ethereum’s Casper are an evolutionary step that connects Bitcoin’s permissionlessness with decades of knowledge of fault tolerant distributed systems.

This is only a partial story. We still need the second strand: the introduction of economics and markets into computer science and engineering.

Returning to the history of computing’s earliest days, the institutions that hosted the large expensive machines of the 1950s and 1960s needed to manage the demand for those machines. Many institutions used sign-up sheets, some even had dedicated human dispatchers to coordinate and manage a queue. Timesharing systems tried to spread the load on the machine so multiple users could work at the same time.

It was not long before some researchers realised that sharing time on a machine was fundamentally a resource allocation problem that could be tackled by with relative prices. By the late 1960s Harvard University was using a daily auction to reserve space on their PDP-1 machine using a local funny money that was issued and reissued each day.

As the industry shifted from a many-users, one-computer structure to a many-users, many-distributed-computers structure, the computer science literature started to investigate the allocation of resources between machines. Researchers stretched for the appropriate metaphor: were distributed systems like organisations? Or were they like separate entities tied together by contracts? Or were they like markets?

In the 1988 Agoric Open Systems papers, Mark S. Miller and K. Eric Drexler argued not simply for the use of prices in computational resource allocation but to reimagine distributed systems as a full-blown Hayekian catallaxy, where computational objects have ‘property rights’ and compensate each other for access to resources. (Full disclosure: I am an advisor to Agoric, Miller’s current project.) As they noted, one missing but necessary piece for the realisation of this vision was the exchange infrastructure that would provide an accounting and currency layer without the need for a third party such as a bank. This, obviously, is what Bitcoin (and indeed its immediate predecessors) sought to provide.

We sometimes call Bitcoin the first successful fully-native, fully-digital money, but skip over why that is important. Cryptocurrencies don’t just allow for censorship-free exchange. They radically expand the number of exchange that can occur – not just between people but between machines. Every object in a distributed system, all the way up and down the technology stack, has an economic role and can form distinctly economic relationships. We see this vision in its maturity in the complex economics of resource allocation within blockchain networks.

Any origin story is necessary simplified, and the origin story I have proposed here skips over many key sources of the technology that is now blockchain: cryptography, the history and pre-history of smart contracts, and of course the cypherpunk community from which Bitcoin itself emerged. But I believe this narrative places us on a much sounder footing to talk about the long term social and economic relevance of blockchain.

As Sinclair Davidson, Jason Potts and I have argued elsewhere, blockchains are an institutional technology. They allow us to coordinate economic activity in radically different ways, taking advantage of the global-first, trust-minimised nature of this distributed system to create new types of contracts, exchanges, organisations, and communities. The scale of this vision is clearest when we compare it with what came before.

Consider, for instance, the use of prices for allocating computer time. The early uses of prices were either to recoup the cost of operation for machines, or as an alternative to queuing, allowing users to signal the highest value use of scarce resources. But prices in real-world markets do a lot more than that. By concentrating dispersed information about preferences they inspire creation – they incentivise people to bring more resources to market, and to invent new services and methods of production that might earn super-normal returns. Prices helped ration access to Harvard’s PDP-1, but could not inspire the PDP-1 to grow itself more capacity.

The Austrian economist Ludwig von Mises wrote that “the capitalist system is not a managerial system; it is an entrepreneurial system”. The market that is blockchain does not efficiently allocate resources across a distributed system but instead has propelled an explosion of entrepreneurial energy that is speculative and chaotic but above all innovative. The blockchain economy grows and contracts, shaping and reshaping just like a real economy. It is not simply a fixed network with nodes and connections. It is a market: it evolves.

We’ve of course seen evolving networks in computation before. The internet itself is a network – a web that is constantly changing. And you could argue that the ecosystem of open-source software that allows developers to layer and combine small, shared software components into complex systems looks a lot like an evolutionary system. Neither of these directly use the price system for coordination. They are poorer for it. The economic needs of internet growth has encouraged the development of a few small and concentrated firms while the economic needs of open-source are chronically under-supplied. To realise the potential of distributed computational networks we need the tools of an economy: property rights and a native means of exchange.

Networks can fail for many reasons: nodes might crash, might fail to send or receive messages correctly, their responses might be delayed longer than the network can tolerate, they might report incorrect information to the rest of the network. Human social systems can fail when information is not available where and when it is needed, or if incentive structures favour anti-social rather than pro-social behaviours.

As a 1971 survey of the domain of fault tolerant computing noted “The discipline of fault-tolerant computing would be unnecessary if computer hardware and programs would always behave in perfect agreement with the designer’s or programmer’s intentions”. Blockchains make the joint missions of economics and computer science stark: how to build reliable systems out of unreliable parts.