The dissertation studies how privacy and trust are shaped by digital technologies: how individuals value privacy over personal data, how AI alters trust and disclosure, and how decentralised blockchains can sustainably replace trusted intermediaries. Chapter 1 argues that the 'privacy paradox' --- that individuals claim to value privacy, yet readily disclose personal data --- arises because privacy is treated as monolithic, when it is multidimensional. I develop a framework that distinguishes voluntary disclosure from involuntary data diffusion, reconciling the paradox by showing that disclosures reflect contextual trade-offs. Using a discrete choice experiment, I provide estimates of privacy valuations across both institutional and social contexts. I find that privacy has substantial value when exposure results in harmful consequences, such as socially revealing data reaching close contacts. I also document an AI privacy puzzle: individuals are less concerned about privacy from AI assistants than from the firms that develop them. Chapter 2 examines this AI privacy puzzle. Using a survey experiment, I replicate the finding from Chapter 1 specifically for firms in the AI industry, highlighting the privacy gap that arises despite the clear product--firm relationship. An information treatment that explicitly links AI assistants to their firms increases concern about both, but does not significantly reduce this gap. Instead, the gap also reflects the anthropomorphic features of AI assistants, aversion to the commercial nature of firms, and the trust and perceived control consumers attach to each. However, when respondents evaluate real-world AI assistant--firm pairs, brand familiarity is the strongest predictor of where privacy concern is attributed. Chapter 3 considers decentralised trust in blockchain systems, in which consensus mechanisms replace trusted intermediaries. I propose a 'proof of quiet quitting' consensus mechanism that reduces the excessive energy consumption of proof of work while retaining the decentralisation that proof of stake can compromise. By introducing a participation lottery with unrestricted entry and an endogenous cutoff, the mechanism separates maximum effort capacity from the probability of winning, inducing participants to exert no more than the minimum effort required in equilibrium.
We study a three-stage decision process that consists of information acquisition, project choice, and execution of the selected project. A principal wants to choose and implement a proactive project, and hires an agent who chooses a costly effort at the information acquisition stage as well as a costly effort at the execution stage. What the principal can do at the beginning is the allocation of the formal decision authority over project choice, either to herself or the agent. We show that the principal may choose to delegate decision authority to the agent, however unlikely the interest of the agent is to be congruent with her interest, or however competent and experienced she is. We provide several testable predictions. (i) Delegation is more likely as the manager has discretion over both information acquisition and implementation. (ii) Delegation is less likely as opportunities for compromising improve. (iii) Whether or not the parties agree about the status quo matters: In particular, if their preferences about the default decisions differ, the organization is more likely to be decentralized for new project development as their interests are less likely to be congruent. We further discuss the extent to which our results on optimal delegation survive when artificial intelligence (AI) is deployed, distinguishing autonomous and nonautonomous AI. If AI can fully automate information acquisition or execution, delegation cannot be optimal, but it can be optimal if the agent remains responsible for execution. If AI instead supports execution by lowering its cost, delegation can survive.
Abstract As we look to the future, how might decentralized autonomous organizations (DAOs) evolve? And where, beyond corporate law, might we find guidance for the legal questions those evolved DAOs pose? DAOs are, and will increasingly become, instrumentalities of artificial intelligence (AI). DAOs are connected with AI in at least three ways: They are tools for decentralized governance of AI data and models; AI may be used to automate the management and operations of DAOs; and DAOs themselves may function as a form of AI. As such, DAOs inherit the major regulatory and ethical challenges that AI poses, most notably with regard to autonomy. Thus, to consider the future questions DAOs pose and how to address them, we must look to the raging debates over AI regulation, and connect them to the more established themes of corporate law.
Abstract (Recent Book Presentation â AAG2026, San Francisco, California) This presentation introduces the recent book Datafied Democracies & AI Economics Unplugged , which critically examines how artificial intelligence (AI) is reshaping democracy, sovereignty, and economic systems through data infrastructures. Moving beyond techno-centric accounts, the book situates AI within political economy and innovation systems theory to interrogate how platform capitalism and data-driven governance are transforming contemporary societies into âdatafied democracies.â The book develops a twofold analytical framework. First, it explores smart cities as key sites of technopolitical transformation, where AI infrastructures consolidate power in âdata-opolies,â raising fundamental questions about democratic accountability and representation. While policy initiatives around âtrustworthy AIâ attempt to address these tensions, the analysis demonstrates that technical solutions alone are insufficient without institutional and territorial embedding. Second, the book examines the emergence of network states, algorithmic nations, and alternative forms of sovereignty in a post-Westphalian context. It critically interrogates the promises of Web3 decentralization, showing how they often reproduce new forms of concentration, including crypto-elite dominance and technocratic governance. In response, the book advances data sovereignty as a contested fieldâcontrasting state-centric, corporate, and collective approachesâand positions data cooperatives as a pathway toward democratic data governance. The central argument is that the key challenge of AI economies lies not in technological innovation per se, but in the governance of data infrastructures and their societal implications. Drawing on global case studies, the book ultimately proposes mission-oriented and institutionally grounded innovation systems to reconnect technological development with democratic values, addressing the enduring tension between frontier innovation and social inclusion.
Decentralized autonomous organizations (DAOs) and AI-agent systems combine cryptographic execution with blockchain-based governance, yet observed organizations almost universally combine these mechanisms with a conventional legal entityâa foundation, statutory DAO form, or limited liability wrapper. I develop a stylized model in which token-holders jointly determine wrapper choice and governance concentration, generating multiple equilibria: an inefficient trap in which the wrapper coalition cannot form because no holder will absorb the front-loaded fixed cost alone, and an efficient wrapper equilibrium in which the coalition reaches scale and amortizesfixed costs effectively. The trap is an empirically grounded coordination problem rather than an analytical artifact, and a global-games selection argument identifies the threshold at which institutional design tips the system between equilibria. The framework reframes the CFTC v. Ooki DAO ruling, the Wyoming DAO LLC and DUNA statutes, and AI legal personhood debates as questions of equilibrium selection rather than of substantive cost allocation, and bounds the âCoasean singularityâ claim that AI agents dramatically reduce transaction frictions.
The contemporary digital information ecosystem is suffering from a structural market failure analogous to George Akerlofâs "Market for Lemons." In an era of Generative AI, the marginal cost of producing misinformation has approached zero, while the cost of verifying truth remains high. This asymmetry has created a "Trust Deficit" where high-quality information cannot be reliably distinguished from algorithmic noise. Current remediation strategies are bifurcated between two flawed extremes: Centralized Web2 Platforms (which prioritize scalability at the expense of transparency and are prone to censorship) and Decentralized Web3 Networks (which prioritize immutability but suffer from the "Garbage In, Garbage Out" paradox - permanently recording unverified data). The Trust-Scalability Trilemma: This research posits that decentralized reputation systems face a "Trust-Scalability Trilemma," historically unable to simultaneously achieve Veracity (Accuracy), Scalability (Throughput), and Decentralization (Censorship Resistance). Traditional solutions, such as Token Curated Registries (TCRs), have failed because they rely on synchronous, on-chain voting for every data point, resulting in prohibitive latency and gas costs. The Solution: This paper introduces The Klyrox Protocol, a decentralized middleware designed to resolve this trilemma by decoupling Content Execution from Content Verification. The protocol introduces a novel consensus mechanism, "Proof-of-Klyrox," which combines Optimistic Machine Learning (opML) with Game Theoretic Integrity Bonds. Proof-of-Klyrox is not a blockchain consensus mechanism. It is a layered fraud-detection and incentive framework anchored to existing consensus networks. Scope Note: Protocol V1 focuses exclusively on objective, verifiable claims (e.g., market data, timestamped events, quantifiable metrics). Subjective content quality assessment (e.g., editorial judgment, artistic merit) is explicitly out of scope and scheduled for research in future iterations. The system operates on an "Optimistic" presumption of validity: Optimistic Execution: Content is verified instantly via off-chain AI Oracles, reducing verification costs by an estimated 85-95% compared to traditional on-chain governance models. Cryptoeconomic Security: Users must stake financial collateral (Integrity Bonds) to publish. This creates a "Pay-to-Truth" incentive structure where the cost of generating misinformation strictly exceeds the potential profit. Sybil Resistance: The protocol implements a proprietary Time-Decayed Stake-Weighted (TDSW) algorithm. This scoring engine ensures that influence scales logarithmically with capital (preventing plutocratic capture) and decays exponentially over time (preventing the entrenchment of dormant actors). By financializing reputation into a portable, quantifiable asset class defined as "Epistemic Capital," The Klyrox Protocol offers a scalable blueprint for a self-regulating "Market for Truth." It transforms trust from a subjective social sentiment into an objective, verifiable economic product, providing the necessary infrastructure for the next generation of decentralized media, prediction markets, and AI safety layers. Author's Note: This whitepaper outlines the technical architecture and game-theoretic mechanisms underpinning the concept of "Epistemic Capital," as explored in The Algorithmic Monographs series by Ali Sadhik Shaik (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence and The Synthetic C-Cuite).
This Article examines the national security risks posed by intelligent non-fungible tokens, or iNFTs, which combine blockchain-based digital assets with adaptive artificial intelligence. It argues that iNFTs amplify concerns surrounding money laundering, terrorist financing, sanctions evasion, cybercrime, and disinformation while creating difficult cross-border problems of jurisdiction, choice of law, and enforcement. The Article concludes that current AML/KYC frameworks are inadequate and calls for updated legislation, international regulatory harmonization, and AI-enabled blockchain analytics.
While Decentralized Autonomous Organizations (DAOs) and Artificial Intelligence are reshaping the governance of academic societies, reliably integrating on-chain decisions with off-chain physical activities remains a critical challenge. The fundamental bottleneck is the difficulty of reliably integrating real-world execution outcomes into the digital decision-making loop. To address this, we propose an endogenous contribution evaluation framework integrating Decentralized Physical Infrastructure Networks (DePIN) and Vision-Language-Action (VLA) models. This approach maps physical entities to on-chain decentralized identities. By leveraging VLA edge nodes to analyze multimodal behavioral data collected via DePIN, the system autonomously generates a verifiable Proof of Real-World Contribution (PoRWC). This proof subsequently drives on-chain incentive distribution through a reputation-weighted consensus mechanism. Consequently, this framework establishes an endogenously trustworthy closed loop from physical processes to digital governance. We demonstrate its feasibility and scalability through a case study of the Chinese Association of Automation (CAA), providing a robust engineering path for the parallel governance of modern academic societies.
Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holdersâ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLOâs multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems.
This study examined the role of visual design in Non-Fungible Tokens (NFTs) as a strategic component of value creation and inclusive participation within the decentralized creative economy. The research addressed the growing need to understand how visuals influence identity, accessibility, and community-driven economic engagement in digital platforms. The study was conducted using a qualitative interpretive approach, employing visual rhetorical analysis and digital ethnography. Ten top NFT collections on the OpenSea marketplace were analyzed based on their visual characteristics, creator inclusion, and community interaction. The visual elements were assessed through rhetorical lenses ethos, pathos, and logos, while participation was observed through social media metrics and community discourse. The findings indicated that accessible and culturally resonant visual designs enhanced user engagement and contributed to broader economic inclusion. Collections with visually open, emotionally engaging, and culturally inclusive features attracted stronger community involvement and sustained market relevance. In contrast, NFTs with elitist or exclusive visual narratives demonstrated more limited participation and accessibility. The study concluded that visual identity plays a central role in shaping the success of NFTs not only as aesthetic artifacts but as instruments of economic democratization. It was recommended that future research explore algorithmic visual production and long-term community dynamics across diverse cultural contexts. This research highlighted the potential of visual design to foster equity, representation, and sustainable value in the evolving digital economy.
This paper presents an ontology-driven administrative monitoring system that integrates blockchain smart contracts to ensure transparency, accountability, and integrity in organizational processes.This tracks resources and manages administrative workflows in an open and decentralised manner, addressing long-standing governance challenges such as opacity and unaccountability.Many important administrative processes involve the movement of resources from one point to another; these resources and processes can be modelled similarly to the movement of goods in a supply chain.Motivated by the need to improve institutional governance, particularly in contexts where individual actions often undermine fairness, this study combines ontology and blockchain to formalize administrative processes and enhance traceability.A pre-created ontology from the author's previous work, developed for postgraduate administration at the University of Ibadan, was adopted.The ontology defines classes and activities which were translated into blockchain entities and smart contracts, implemented in Solidity, and deployed on the Ethereum test network.Test cases derived from ontology competency questions validate the functional correctness of the smart contracts.The results confirm that administrative activities can be monitored transparently and immutably, providing a foundation for broader applications in public administration, education, and corporate governance.
This paper explores how Decentralized Autonomous Organizations (DAOs) could inform and shape participatory procedures in democratic governance. We apply DAO decision-making, such as rule-based input aggregation, transparent participation, and programmable decision-making, to a real-world case: the legislative development of the Swiss E-ID law, a proposal to establish a digital identity system for secure online authentication for Swiss residents. Using data from the official legislative consultation, we simulate how DAO-inspired mechanisms could have altered the aggregation of input and policy outcomes. Our analysis contributes conceptually and empirically to debates on digital democratic innovations, showing how programmable governance can be used not only to design new institutional forms, but also to critically assess the procedural dynamics of existing ones.
Rana Hassam Ahmed, Muhammad Zeeshan, Unais Ali, Muhammad Sarfraz Khan ¡ 7 authors
Smart contracts power decentralised applications, but once deployed, their flaws stay exploitable. Existing fuzzers such as ConFuzzius, Smartian, and VULSEYE use hybrid static and dynamic analysis but depend on fixed heuristics and lack adaptive learning. AI-FUZZ is an adaptive machine learning guided fuzzing framework that pairs deep reinforcement learning with stateful graybox fuzzing. It learns from execution traces to improve input generation, focus on high-risk contract states, and cut redundant executions. The framework also includes static analysis, adaptive mutation, and an oracle-based validation to boost accuracy and reduce false positives. Tested on 42,738 real-world contracts, AI-FUZZ achieved a 96.8% true positive rate, 4.2% false positive rate, 27% higher detection coverage than leading fuzzers, and a 33% reduction in average detection time. It scales across small, medium, and large contracts and offers a self-improving, efficient, and reliable approach for large-scale blockchain security audits.
Artificial intelligence increasingly governs access to credit, employment, and identity verification, raising questions of rights protection when deployed across borders. This paper develops a computable framework for Fundamental Rights Impact Assessment (FRIA) that transforms the legal principles of necessity and proportionality into quantifiable metrics. By embedding these standards into algorithmic pipelines, the framework enables verifiable auditing of high-risk AI systems. Simulations were conducted in two domains, credit scoring and biometric authentication, using synthetic datasets modeled on European and non-European jurisdictions. The necessity audits reduced the average input set by 24.6 Âą 2.3 variables while sustaining predictive accuracy, while proportionality assessments exposed heavy reliance on sensitive features in 39%* of credit scoring models and significant subgroup disparities in biometric authentication. Distributed verification protocols preserved results on blockchain ledgers, ensuring transparency and cross-border accountability. The findings demonstrate that computable FRIAs can operationalize fundamental rights obligations, producing results that can be inspected by regulators and reviewed in courts. The study concludes that computable methods offer a practical bridge between jurisprudential principles and algorithmic implementation, though persistent divergences in cross-border proportionality standards remain a major challenge for harmonized enforcement.
National identity systems require efficient, equitable decision-making that safeguards personal data. This article proposes a Self-Sovereign Identity (SSI) architecture, supported by a Verify-Without-Reveal (VWR) framework, designed for national-scale implementation. SSI places credentials in a citizen wallet and enables selective disclosure and zero-knowledge proofs, so services can verify attributes without seeing underlying records. VWR adds the policy and accountability spine: yes/no attribute APIs for holder-absent cases, purpose-bound and zero-trust enforcement on every call, and an immutable audit layer on a permissioned ledger. The study synthesises current standards and leading implementations in Europe and worldwide and formulates a deployable blueprint with clear roles, consent and lawful-override flows, per-agency pseudonyms, and regulator and citizen visibility. The study outlines reference APIs, user experiences for wallets and verifiers, and performance metrics suited for national workloads. Privacy-preserving AI strengthens biometric liveness, fraud detection, and anomaly response without centralising sensitive data. The framework aligns with GDPR data minimisation and purpose limitation, supports the European Digital Identity Wallet, and meets high-risk AI governance requirements. Results show how SSI proofs and VWR controls reduce unconsented disclosure and cross-agency browsing, while keeping latency low and interoperability high. The contribution is both conceptual and operational: a phased migration path that turns verify-without-reveal into the default mode for government and regulated services, improving security, inclusion, and public trust.
This paper reviews the innovative applications of AI and Web3 in metaverse social platforms. It first analyzes the foundational roles of AI (e.g., virtual avatar generation, intelligent interaction, personalized recommendation) and Web3 (e.g., blockchain, NFTs, decentralized identity) in enabling immersive, secure, and user-centric social interactions. It then examines their synergies, with case studies of Decentraland and The Sandbox illustrating practical integrations. The research identifies key challenges, including technical bottlenecks (e.g., AI realism, blockchain scalability), user-related issues (e.g., awareness, privacy concerns), and industry-level hurdles (e.g., regulatory ambiguities, homogenization). Finally, it proposes future directions: advancing AI/Web3 technologies, expanding application scenarios across education and entertainment, and implementing strategic recommendations to foster inclusive and sustainable metaverse social ecosystems.
This paper addresses the critical need for accountability in artificial intelligence (AI) systems, particularly in domains where decisions have significant societal and ethical implications. We propose a novel framework leveraging auditable attestations to ensure provable compliance with predefined standards and regulations. The core of our approach involves generating verifiable proofs about the behavior and characteristics of machine learning models, allowing for independent audits and assessments. We explore the theoretical foundations of such attestations, focusing on cryptographic techniques like zero-knowledge proofs and secure multi-party computation, which enable the verification of model properties without revealing sensitive information. Furthermore, we discuss the practical implementation of our framework, including the design of attestation protocols, the selection of relevant model properties to verify, and the development of tools for generating and validating attestations. We illustrate the effectiveness of our approach through case studies in areas such as fairness in lending, transparency in healthcare, and safety in autonomous driving. Our results demonstrate the potential of auditable attestations to enhance trust and accountability in AI systems, fostering responsible innovation and deployment.
Arthur Carvalho, Liudmila Zavolokina, Suman Bhunia, Gerhard Schwabe
Regulatory changes have enabled American student-athletes to profit from their name, image, and likeness (NIL). However, only a fraction of the student-athlete population is actually profiting from their NIL, which raises questions concerning fairness and inclusiveness. Motivated by that scenario, we look at technological solutions capable of sharing a limited amount of financial resources fairly and inclusively. Following a design science methodology, we define design requirements for such technological solutions after interviewing student-athletes, which leads us to establish the inclusive-meritocratic fairness criterion. Subsequently, we determine design principles that artifacts aiming at helping student-athletes should satisfy. We find that a solution that satisfies the proposed design principles is to associate student-athletes with digital collectibles represented as non-fungible tokens (NFTs). The core idea behind our artifact is that student-athletes receive royalties in primary markets after NFTs are randomly minted, plus deterministic royalties in secondary markets whenever a transaction involving their collectibles happens. Interviews with student-athletes validate our design. We conclude the paper by discussing how our ideas give rise to a new NIL design theory.
Open access
Digital Games and Media
Ethics and Social Impacts of AI
Consumer Behavior in Brand Consumption and Identification
Third-party risk management (TPRM) reaches an inflection point, with artificial intelligence (AI) capabilities meeting pressing demands for real-time vendor risk oversight of increasingly complex digital ecosystems. Conventional assessment methodologies resting on manual questionnaires, annual review cycles, and document-centric evaluations are poorly matched to the pace and interconnectedness driving modern technology. This article analyzes how intelligent automation is remaking basic processes in vendor governance, from optimization of questionnaires through semantic modeling to predictive monitoring allowed through continuous data synthesis. Unstructured vendor control documentation is now parsed by natural language models to extract control metadata and produce risk assessments that must be validated, rather than created, by humans. Algorithmic integrity is tackled with multi-model verification architectures that employ parallel processing pipelines where ensemble methods quantify confidence levels and flag gaps in the vendor control environment for risk subject matter expert review. Brain-inspired computing principles underpin system design, with hierarchical feature extraction possible, along with adaptive learning from assessment outcomes. Technical debt becomes a critical governance factor, particularly in the context of data dependencies and configuration management across model lifecycles. Explainable artificial intelligence provides transparency that is vital to regulatory recognition, allowing risk officers to trace decision pathways and understand feature attributions underlying automated recommendations. Convergence of distributed ledger technology with intelligent risk systems unlocks opportunities for tamper-proof audit trails and privacy-preserving attestations in support of cross-organizational governance frameworks framed by emerging digital resilience mandates.
This research examines how emerging forms of digital sovereignty, decentralized infrastructures, and anticipatory AI governance are reshaping nationhood in the algorithmic age. Drawing on the conceptual framework of Algorithmic Nations (Calzada 2018) and incorporating new empirical insights from embedded action research (2022â2025), the study analyses the Basque Country as a paradigmatic case of a âsmall stateless nationâ navigating the global reconfiguration of power between states, corporations, and communities. The presentation synthesizes three competing post-Westphalian paradigmsâNetwork States (Srinivasan 2022), Network Sovereignties (De Filippi 2024), and Algorithmic Nations (Calzada 2018)âas shown in the comparative table on page 19, highlighting their differing assumptions regarding governance, identity, participation, and technological control. Building on the diagnostic indicators of Europeâs digital dependence (page 10) and the transition from Gaia-X to EuroStack (page 11), the study evaluates the strategic implications of digital public infrastructures, data cooperatives, federated architectures, and Web3 ecosystems for stateless nations. Through comparative analysis of the Global North (e.g., Scotland, Quebec, Flanders), the Global South (e.g., Kurdistan, SĂĄmi, Tamil, Amazigh), and the Basque Country (pages 16â17), the work demonstrates how communities with diverse geopolitical constraints can articulate forms of AI sovereignty grounded in rights-based, culturally rooted, and community-driven governance. The Basque case illustrates how fragmented digital systems (.eus, EJIE/Izenpe, Osakidetza, MUBIL, etc.) can evolve toward an interoperable, multi-scalar technopolitical architecture, aligning linguistic, territorial, and infrastructural dimensions. The analysis argues that AI-driven infrastructures, data governance, and decentralized architectures are not merely technical layers but emerging geopolitical terrains where stateless, indigenous, diasporic, and minority nations can renegotiate autonomy. The concept of Algorithmic Nations provides a framework for understanding how community sovereignty can be built through data commons, federated systems, and anticipatory governance, particularly in multilingual and culturally distinct territories such as the Basque Country. Overall, the study contributes to debates on global digital governance, digital sovereignty, and the future of nationhood by proposing that algorithmic infrastructures are becoming central to political organization. It calls for democratic, inclusive, and community-oriented models of AI governance capable of avoiding techno-authoritarianism, Big Tech dependency, and âsovereignty washing,â while enabling emancipatory, culturally anchored, and future-oriented forms of collective self-determination.
Democratic institutions increasingly rely on verifiable digital trust to enable fair participation and evidence-based decisions. Truvry is a decentralised protocol that converts behaviour-based evidence (usage patterns, transaction integrity, peer attestations) into portable cryptographic proofs that remain independent of any single platform or identifier, allowing individuals to transfer trust capital across domains while preserving privacy. The current prototype is zero-knowledgeâcompatible; in this version we use hashed proof anchoring and field-level redaction (no zk-SNARK module is deployed), with configurable smart-contract verifiers. By decoupling trust from identity, Truvry widens citizen inclusion, mitigates gatekeeping bias, and supplies auditable inputs for AI-mediated governance. In prototype tests (n=112), end-to-end proof issuance averaged 3.7 s (fastest local 1.4 s), verifier parse+check averaged 1.8 s, and the current minimum anonymisation entropy is 8.9 bits; gas costs for optional on-chain anchoring remained below US$0.02. All results are based on simulated user streams; a production pilot is planned.
This thematic issue examines how artificial intelligence, metaverse imaginaries, and decentralized Web3 systems have become arenas for states to build infrastructures, set technical standards, and project geopolitical power. It reconceptualizes technology not merely as an object of regulation but as a medium of statecraft through which sovereignty, security, and leadership are contested and remade in a multipolar digital order. This issue analyzes three interconnected dimensions: (a) the impact of global AI competition on state-making processes, enhancing coercive, extractive, delivery, and informational capacities similar to earlier state formation phases; (b) the nature of technological leadership as a relational and dynamic process influenced by interactions between leading and following states; and (c) the role of security logics in transforming external rivalry and internal governance through securitization. Through comparative analysis of the US, China, the EU, and emerging economies, this issue explores how diverse political systems encode openness, sovereignty, and accountability into their technological regimes, demonstrating that technological governance is inseparable from state-making. The contributions map competing logicsâsovereign, liberal, entrepreneurialâshowing that digital governance emerges not as convergence toward a singular model but as recursive entanglements of imagination and infrastructure.