Submitted to the to the Senate Standing Committee on Economics. With Aaron Lane, Darcy WE Allen, Elizabeth Morton, Max Parasol, and Jason Potts. Available here.
With Jason Potts, Darcy W E Allen, and Nataliya Ilyushina. Available on SSRN.
Large Language Models (LLMs) or generative AI have emerged as a new general-purpose technology in applied machine learning. These models are increasingly employed within firms to support a range of economic tasks. This paper investigates the economic value generated by the adoption and use of LLMs, which often occurs on an experimental basis, through two main channels. The first channel, already explored in the literature (e.g. Eloundou et al. 2023, Noy and Wang 2023), involves LLMs providing productive support akin to other capital investments or tools. The second, less examined channel concerns the reduction or elimination of agency costs in economic organisation due to the enhanced ability of economic actors to insource more tasks. This is particularly relevant for tasks that previously required contracting within or outside a firm. With LLMs enabling workers to perform tasks in which they had less specialisation, the costs associated with managing relationships and contracts decrease. This paper focuses on this second path of value creation through adoption of this innovative new general purpose technology. Furthermore, we examine the wider implications of the lower agency costs pathway on innovation, entrepreneurship and competition.
The benefits of regulation should exceed the cost of regulating. This paper investigates the impact of widespread general-purpose computing on the cost of enforcing of regulations on generative artificial intelligence (AI) and decentralized finance (DeFi). We present a simple model illustrating regulators’ preferences for minimising enforcement costs and discuss the implications of regulatory preferences for the number and size of regulated firms. Regulators would rather regulate a small number of large firms rather than a large number of small firms. General-purpose computing radically expands the number of potentially regulated entities. For Defi, the decentralized nature of blockchain technology, global scale of transactions, and decentralised hosting increase the number of potentially regulated entities by an order of magnitude. Likewise, locally deployed open-source generative AI models make regulating AI safety extremely difficult. This creates a regulatory dilemma that forces regulators to reassess the social harm of targeted economic activity. The paper draws a historical comparison with the attempts to reduce copyright infringement through file sharing in the early 2000s in order to present strategic options for regulators in addressing the challenges of AI safety and DeFi compliance.
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.
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.
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.”
With Vijay Mohan and Peyman Khezr. Available at SSRN
Abstract: Blockchain applications are increasingly experimenting with novel governance mechanisms that address issues that are important for their community: resistance to voter fraud in the form a Sybil attack; resistance to the formation of a plutocracy within the community; and, the ability to express preference intensity. In this paper, we take a closer look at these issues confronting decentralized governance. Our contribution is three-fold: first, we lay some analytical foundations for the formal modelling of the necessary and sufficient conditions for a voting system to be resistant to a Sybil attack; second, we show that a voting mechanism with a single instrument for expressing preference intensity, such as the quantity of tokens, cannot simultaneously achieve resistance to both Sybil attacks and plutocracy formation; and third, we design a voting mechanism, bond voting, that is Sybil resistant and offers a second instrument of voting influence (time commitment) for plutocracy resistance.
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.
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.
Working paper with Jason Potts, Darcy W E Allen, Aaron M. Lane and Trent MacDonald. Available on SSRN
Abstract: Blockchains have enabled innovation in distributed economic institutions, such as money (e.g. cryptocurrencies) and markets (e.g. DEXs), but also innovations in distributed governance, such as DAOs, and new forms of collective choice. Yet we still lack a general theory of blockchain governance. James Buchanan once described public choice theory as ‘politics without romance’ and argued instead for an exchange theory of politics. Following Buchanan, we argue here for an exchange view of blockchain governance. The ‘romantic’ view of blockchain governance is collective choice and consensus through community voting. The exchange view, instead, is focused on entrepreneurial discovery of opportunities for value creation in governance space through innovation in protocols (e.g. Curve, Convex, Lido, Metagov, etc) that facilitate exchange of coordination and voting rights, that are newly made possible through tools that enable pseudonymous, composable and permissionless governance actions. The exchange lens on web3 governance also helps illuminate how this emergent polycentric process can generate robustness in decentralised systems.