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The internet needs 'Agentic Citizens' — persistent, identity-bearing AI agents that collaborate across platforms — and the authors propose Synergy, a runtime and architecture to make that vision feasible, though the design remains untested at scale.
n/a theoretical arxiv
Synergy: A Next-Generation General-Purpose Agent for Open Agentic Web · Xiaohang Nie, Zihan Guo, Kezhuo Yang, Zhichong Zheng, Bochen Ge, Shuai Pan, Zeyi... · Mar 30, 2026
Countries speak the same AI ethics language but govern very differently: a comparative analysis of 24 policy texts finds five distinct models of AI governance—from Europe’s rights-first approach to China’s state-centric model—driven by underlying political-economic institutions rather than convergent ethical principles.
low descriptive semantic_scholar
Artificial intelligence governance and social policy divergence: a comparative political economy perspective on global AI regulation · Deepak Kumar, Chinmaya Kumar Sahu · Fetched Mar 30, 2026
Agentic AI could halve investment-management costs and threaten large-scale displacement among financial planners; the authors propose mandatory AI literacy, specialized degree tracks, federal retraining and income support, and regulatory incentives to steer a 5–15 year workforce transition.
low descriptive semantic_scholar
STRENGTHENING FINANCIAL WORKFORCE COMPETITIVENESS: A CURRICULAR FRAMEWORK FOR INTEGRATING AGENTIC AI INTO FINANCIAL PLANNING EDUCATION · Satyadhar Joshi · Fetched Mar 30, 2026
Agentic AI is only beginning to surface in U.S. finance firms' annual reports—0.4% of firms in 2024 and 1.6% in 2025—but those that mention agentic systems tend to surround them with denser governance and safety language, implying deployments are currently concentrated where governance is more mature.
n/a descriptive semantic_scholar
Measuring agentic AI adoption and control frameworks in finance · Atta ul Mustafa, A. Aysan · Fetched Mar 30, 2026
A model warns that consumer demand switching to AI services can widen wage gaps in non-traded sectors, but Indian transport data show no clear post-2007 spike in inequality — uptake of AI ride services stayed below 0.7%, muting measurable effects.
low theoretical semantic_scholar
Artificial Intelligence, Demand Switching and Sectoral Wage Gap · Shreya Roy, Bibek Ray Chaudhuri · Fetched Mar 30, 2026
Making an AI transparent doesn't automatically win more data: transparency raises stated willingness to share only for users who already trust AI, while immediate sharing decisions remain high and unchanged across human, white-box, and black-box scenarios.
medium rct openalex
Understanding Data-Sharing with AI Systems: The Roles of Transparency, Trust, and the Processing Entity · Julian Rosenberger, Sophie Kuhlemann, Verena Tiefenbeck, Mathias Kraus, Patrick ... · Fetched Mar 30, 2026
AI in research proposals delivers only modest immediate publication gains concentrated among top projects, but substantially reorganizes how science is done: AI-enabled projects allocate more to people, form larger teams, and broaden ideation and experimental activities, signaling potential future productivity gains as models improve.
medium correlational arxiv
Artificial Intelligence in Science: Returns, Reallocation, and Reorganization · Moh Hosseinioun, Brian Uzzi, Henrik Barslund Fosse · Mar 30, 2026
High AI reliance among listed new-energy and auto firms correlates with greater financial risk rather than improved financial health; firms that effectively convert R&D into patents or intelligent equipment avoid the worst effects.
low correlational openalex
The 'Intelligent Trap' in Corporate Finance—A Study Based on New Energy Vehicle Enterprises · Qingzhi Zeng · Mar 30, 2026
Robot adoption is linked to stronger sectoral value creation in the EU, with the biggest gains concentrated in top-performing industries; uneven returns to R&D and specialist human capital imply automation policies should be sector-specific and paired with targeted reskilling.
low correlational openalex
Automation and growth in the European Union: sectoral insights from robot density analysis · Nicoleta Mihaela Doran, Gabriela Badareu, Dorin Mădălin Dogaru, Cosmin Alexandru... · Mar 30, 2026
Transformative AI will likely be many, not one: diverse teams of specialized, epistemically distinct AI agents are better positioned to drive deep innovation than lone superintelligences because diversity broadens search, delays premature consensus, and enables unconventional solutions.
n/a theoretical openalex
The Future of AI is Many, Not One · Daniel J. Singer, Luca Garzino Demo · Mar 30, 2026
A 'cognitive commonwealth' of humans and AI could amplify discovery and resilience by enabling transparent, diverse information exchange; sustaining it will require new verification and governance institutions to prevent erosion of trust.
n/a theoretical openalex
A Case for Coevolution · Serge Dolgikh · Mar 30, 2026
China’s rise in AI appears to narrow regional carbon-emissions gaps by improving energy efficiency and environmental monitoring, but deep structural divides persist—green innovation remains coastal and has not yet reduced inland–coastal emission inequality.
medium correlational openalex
Artificial intelligence, green innovation, and regional carbon inequality: evidence from Chinese provincial data · Xiangjun Fan · Mar 30, 2026
Teaching startups how to reorganize around AI nearly doubles short-run revenue and raises customer acquisition by 18%, driven by a 44% rise in discovered AI use cases; growth occurs without proportional increases in labor and with lower external funding needs.
high rct manual
Mapping AI into Production: A Field Experiment on Firm Performance · Hyunjin Kim, Dahyeon Kim, Rembrand Koning · Mar 30, 2026
A simple revealed-preference model shows AI choices can be decomposed into human and machine preference components, and the degree of alignment can be recovered from choice-data; observing human choices makes recovery trivial, but even AI-only data suffices under generic menu variation.
n/a theoretical arxiv
A Revealed Preference Framework for AI Alignment · Elchin Suleymanov · Mar 29, 2026
AI that simply streamlines clinical tasks will not by itself ease hospital capacity pressures; only tools or procurement that change who bears risk or make outcomes more observable can alter entrenched system behaviour, so leaders must pair AI with governance and incentive reform.
low theoretical arxiv
Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective · Ari Ercole · Mar 29, 2026
AI is likely to rewire how jobs are created — moving from employer-dominated hiring to a decentralized ecosystem where individuals leverage AI and platforms to generate work; policy and education should pivot toward cultivating human–AI collaboration and creative skills.
low theoretical arxiv
AI Civilization and the Transformation of Work · Dongsoo Han · Mar 29, 2026
Companies that treat AI as a strategic operating layer — deploying agentic workflows and localized models — can accelerate innovation and expand internationally without traditional physical footprints. This advantage, however, only materializes when firms transition from pilot projects to integrated, governed AI‑first business models.
n/a descriptive openalex
The AI Advantage: Strategic Innovation and Global Expansion in the Digital Era · Yashaswini Bergal, Rupali Dilip Taru · Mar 29, 2026
Indonesia's labor law is unprepared for AI-driven disruption: current statutes emphasize post-termination compensation but lack clear duties for retraining or mechanisms to share AI-driven productivity gains, prompting calls for legal reform to embed lifelong learning, social protection, and fiscal instruments such as automation taxes.
n/a descriptive openalex
Reformasi Hukum Ketenagakerjaan di Era Artificial Intelligence: Perlindungan Pekerja, Reskilling, dan Model Kompensasi Inovatif di Indonesia · Muhammad Urifianto Ardhan, Gunardi Lie · Mar 29, 2026
Kenyan commercial banks that report greater digital and AI adoption also show higher market share, return on equity and customer satisfaction; however, the result is based on cross-sectional correlations from executive surveys rather than a causal research design.
low correlational openalex
Technology Innovation Strategy and the Competitiveness of Kenyan Commercial Banks · Brighton Kithendu Mwanzia, Peter Ngatia, Robert Gichira, Teresia Kyalo · Mar 29, 2026
A 'novelty bottleneck' limits how much AI can shrink human effort: when any fraction of a task requires genuinely novel human judgment, that serial component forces human effort to scale linearly with task size, so stronger agents cut costs but do not change the fundamental scaling. Consequently, organizations can shorten wall-clock time via parallel teams and need fewer humans as agents improve, but aggregate human effort remains bounded—making AI more effective at exploiting existing knowledge than at accelerating frontier research.
n/a theoretical arxiv
The Novelty Bottleneck: A Framework for Understanding Human Effort Scaling in AI-Assisted Work · Jacky Liang · Mar 28, 2026
EpochX proposes treating humans and AI agents as peers in a credits-native marketplace that turns every completed task into reusable assets—skills, workflows, and execution traces—so work can be delegated, verified and monetized at scale. By coupling an explicit asset dependency graph with a credit settlement mechanism, the design aims to make durable human–agent collaboration economically viable despite real compute costs.
n/a theoretical arxiv
EpochX: Building the Infrastructure for an Emergent Agent Civilization · Huacan Wang, Chaofa Yuan, Xialie Zhuang, Tu Hu, Shuo Zhang, Jun Han, Shi Wei, Da... · Mar 28, 2026
An AI multi-agent system cuts TensorFlow-to-JAX migration time roughly 6–8x on commercial Google workloads by combining planners, orchestrators and AI judges; the approach speeds model framework transitions but rests on proprietary tooling and case selection.
medium descriptive arxiv
A Multi-agent AI System for Deep Learning Model Migration from TensorFlow to JAX · Stoyan Nikolov, Bernhard Konrad, Moritz Gronbach, Niket Kumar, Ann Yan, Varun Si... · Mar 28, 2026
AI delivers bigger growth payoffs where institutions adapt: countries with stronger institutions, higher digital capacity and R&D intensity see superior productivity performance, suggesting AI's benefits depend on institutional and organizational innovation.
low theoretical semantic_scholar
Artificial intelligence, institutional innovation and economic growth: A conceptual framework · Zehra Doğan Çalışkan · Fetched Mar 28, 2026
AI-powered, multi-source horizon scanning classifies technologies into emerging, developing and mature stages and matches each stage to tailored policy tools—R&D funding, regulatory sandboxes, procurement incentives and tax relief. Expert Delphi validation and AHP weighting increase prioritization confidence, though outcomes depend on data coverage and expert selection.
low descriptive semantic_scholar
Emerging Technologies Based on Large AI Models and the Design of Support Policies: Patent Mining and Industry Trends · Yiwei Zhang, Shanghai Mu, Baitong Liu · Fetched Mar 28, 2026
AI sharpens ESG scoring and enables real‑time tracking of environmental and social risks, but gains are uneven: poor data, algorithmic bias and weak governance hamper reliable, trustworthy sustainable finance.
medium review_meta openalex
Artificial intelligence in sustainable finance and Environmental, Social, and Governance (ESG) performance: A review · Vibhavari Prasad Mane · Mar 28, 2026