Category: Working paper

Open problems in DAOs

Available at arXiv. With Joshua Tan, Tara Merk, Sarah Hubbard, Eliza R. Oak, Helena Rong, Joni Pirovich, Ellie Rennie, Rolf Hoefer, Michael Zargham, Jason Potts, Reuben Youngblom, Primavera De Filippi, Seth Frey, Jeff Strnad, Morshed Mannan, Kelsie Nabben, Silke Noa, Elrifai, Jake Hartnell, Benjamin Mako Hill, Tobin South, Ryan L. Thomas, Jonathan Dotan, Ariana Spring, Alexia Maddox, Woojin Lim, Kevin Owocki, Ari Juels, and Dan Boneh.

Abstract: Decentralized autonomous organizations (DAOs) are a new, rapidly growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the DAO ecosystem where existing gaps might be tackled through a new data set or by applying tools and ideas from existing research fields such as political science, computer science, economics, law, and organizational science. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the wider research community to join the global effort to invent the next generation of organizations.

Common knowledge theory of stablecoins

With Chloe White and Jason Potts. Available at SSRN.

Abstract: We propose a new theory of stablecoins based on common knowledge. We contrast this with the ‘better money’ theory of stablecoins, which emphasises marginal improvements over the standard origin of money theory as: medium of exchange, unit of account, store of value.

Managing Generative AI in Firms: The Theory of Shadow User Innovation

With Julian Waters-Lynch, Darcy WE Allen, and Jason Potts. Available at SSRN.

Abstract: This paper explores the management challenge posed by pervasive and unsupervised use of generative AI (GenAI) applications in firms. Employees are covertly experimenting with these tools to discover and capture value from their use, without the express direction or visibility of organisational leaders or managers. We call this phenomenon shadow user innovation. Our analysis integrates literature on user innovation, general purpose technologies and the evolution of firm capabilities. We define shadow user innovation as employee-led user innovation inside firms that is opaque to management. We explain how this opacity obstructs a firm’s ability to translate the use of GenAI into visible improvements in productivity and profitability, because employees can currently privately capture these benefits. We discuss potential management responses to this challenge, outline a research program, and offer practical guidance for managers.

Institutions to constrain chaotic robots: why generative AI needs blockchain

With Sinclair Davidson and Jason Potts. Available at SSRN

Abstract: Generative AI is a very powerful new computing technology, but the problem of how to make it economically useful (Alice: “hello LLM, please send an email to Bob”) is limited by its inherent unpredictability. It might send the email, but it might do something else too. As a consequence, the large language models that underpin generative AI are not safe to use for most economically useful and valuable interactions with the world. This is the ‘economic alignment’ problem between the AI as an ‘agent’ and the human ‘principal’ who wants the LLM to interact in the world on their behalf. The answer we propose is smart contracts that can take LLM outputs and filter them as deterministic constraints. With smart contracts, LLMs can interact safely in the real world, and can unlock the vast economic opportunity of economically aligned and artificially intelligent agents.

The exchange theory of web3 governance

With Jason Potts, Darcy W E Allen, Aaron M. Lane and Trent MacDonald. Published in Kyklos,  June 2023. Working paper available on SSRN

Abstract: Blockchains have enabled innovation in distributed economic institutions, such as money (e.g., cryptocurrencies) and markets (e.g., decentralised exchanges), but also innovations in distributed governance, such as decentralised autonomous organisations. These innovations have generated academic interest in studying web3 governance, but as yet there is no general theory of web3 governance. In this paper, we draw on the contrast between a ‘romantic view’ of governance (characterised by consensus through community voting) and the ‘exchange view’ of governance from public choice theory (characterised by an entrepreneurial process of bargaining and exchange of voters under uncertainty). Our analysis is the first to argue that the latter ‘exchange view’ of governance is best to understand the dynamics of governance innovation in web3, providing the foundations for a new general theory of governance in this frontier field. We apply the ‘exchange view’ of governance to three case studies (Curve, Lido and Metagov), exploring how these projects enable pseudonymous, composable and permissionless governance processes to reveal value. Our approach helps illuminate how this emergent polycentric governance process can generate robustness in decentralised systems.

Large language models reduce agency costs


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 problem of ubiquitous computing for regulatory costs

Working paper on SSRN

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.

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.

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.

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.