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.

Voting with time commitment for decentralized governance: Bond voting as a Sybil-resistant mechanism

With Vijay Mohan and Peyman Khezr. Published in Management Science, online March 2024. Early version available at SSRN

Abstract: In this paper, we examine the usefulness of time commitment as a voting resource for decentralized governance when the identity of voters cannot be verified. In order to do so, we take a closer look at two issues that confront token-based voting systems used by blockchain communities and organizations: voter fraud through the creation of multiple identities (Sybil attack) and concentration of voting power in the hands of the wealthy (plutocracy). Our contribution is threefold: first, we lay analytical foundations for the formal modeling of the necessary and sufficient conditions for a voting system to be resistant to a Sybil attack; second, we show that tokens as the only instrument for weighting votes cannot simultaneously achieve resistance to both Sybil attacks and a plutocracy in the voting process; and third, we design a voting mechanism, bond voting, that is Sybil resistant and offers a second instrument (time commitment) that is effective for countering plutocracy when large token holders also have a relatively high opportunity cost of locking tokens for a vote. Overall, our paper emphasizes the importance of time-based suffrage in decentralized governance.

Not on this website yet. I have a simple explainer of the bond voting mechanism at Substack.

Interoperability

Published in Internet Policy Review, Volume 13, Issue 2 (Glossary of decentralised technosocial systems)

Abstract: Interoperability describes the ability of systems to share services and resources with other systems. It is used in many fields — in the law, in communications and payments systems, in healthcare systems and in military alliances, to name a few — and describes a large number of characteristics from technical standards, to information architecture, to organisational governance. This glossary entry presents a topology of interoperability layers and presents some of the key economic and socio-technical concerns faced by interoperable systems.

Author(s): Internet Policy Review

Journal: Internet Policy Review

Year: 2024

URL: https://policyreview.info/glossary/interoperability

Cite: Chris Berg 2024 “Interoperability.” Internet Policy Review. https://policyreview.info/glossary/interoperability.

Continue reading “Interoperability”

The Governance of Cosmos Interchain Security

With Darcy WE Allen and Sinclair Davidson. Available at SSRN.

Abstract: Interchain security (ICS) allows the Cosmos Hub to provide security to other blockchains (‘consumer chains’) and represents a significant revenue model for the Cosmos Hub. This paper investigates the economic and governance aspects of these ICS agreements with a focus on ensuring that the agreements are value adding and robust. The paper identifies potential risks such as vertical integration, challenges in adapting to incomplete contracts, and opportunism in asset-specific investments. It proposes recommendations to enhance the sustainability of ICS relationships, including the establishment of individual governance bodies for each ICS agreement, strategies to manage foreign exchange risks, and a decision tree for the Cosmos Hub to assess new consumer chains. A draft template for consumer chain onboarding is also presented, detailing essential elements like governance, payment terms, and exit clauses. This paper aims to offer actionable insights for improving the governance structures in ICS agreements, thereby fostering robust and enduring interchain security dynamics.

Allocating Capital in Decentralised Networks: Mechanisms for the Cosmos Hub

With Darcy WE Allen and Sinclair Davidson. Available at SSRN.

Abstract: This paper helps allocate shared capital effectively in the Cosmos ecosystem by examining a range of different allocation mechanisms. We identify the core challenges of allocating shared capital – with a focus on knowledge, opportunism and coordination problems. We outline four mechanisms that capital allocation DAOs can use to allocate capital in different contexts: grants, prizes, tenders and in-house production. Each have implications for the transparency and accountability of capital allocation. Our findings help capital allocation DAOs make decisions about how to allocate shared capital across the Cosmos ecosystem.

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.