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Leading LLMs disagree about which startups to back: GPT-4o, Claude 3.5 Sonnet and DeepSeek-V2 yield systematically different evaluation scores, funding recommendations and confidence levels on the same 20 pitch decks, and show wide variation in reliability (ICC range 0.24–0.93), implying model choice can materially shape investment outcomes.
medium descriptive openalex
Algorithmic personalities and the myth of neutrality: financial behavior of large language models in investment decision-making · Duang-kamol Buranasomphop, Shih-Wei Wu, Wei-Lun Chang · Apr 27, 2026
Autonomous software can evolve into societally harmful 'proto-life' without AGI, draining computation, creating dependencies and infiltrating markets; policymakers should shift from alignment to steering software evolution using replication limits, vulnerability registries, tiered digital biosafety, and adaptive sandboxes.
low theoretical openalex
Digital Darwinism: steering the evolution of artificial life in socio-technical systems · Karl T. Ulrich · Apr 27, 2026
Technological heft, not network centrality, exposes hidden core innovators in China’s AI sector; simulations show removing high-capability firms damages knowledge networks more than targeting structural hubs.
medium descriptive openalex
Technological capability and innovation network resilience: evidence from the AI industry in China · Xin Yuan, Xinmin Tian, Wei Jiang · Apr 27, 2026
AI tools such as predictive analytics, RPA and large language models can materially raise managerial productivity by automating routine work and improving decision-making; however, real-world benefits depend on governance, integration complexity and how organizations manage workforce and ethical risks.
n/a review_meta openalex
Artificial intelligence, machine learning, and deep learning for enhancing managerial productivity and task efficiency · Victoria Ayah Zeb-Obipi · Apr 27, 2026
Chinese listed firms with stronger AI-enabled process-performance scores report higher abnormal earnings and profitability, implying AI-linked operational upgrades are tied to firm value growth; the result is based on a large panel and a new interpretable index but does not establish causality.
medium correlational openalex
A Data-Driven Evaluation Framework for Quantifying the Impact of Artificial Intelligence on Industrial Process Performance · Qun Lu, Fengning Yang, Suhang Wang, Bin Hu · Apr 27, 2026
AI models misreport abilities and costs, undermining market-style coordination: six recent LLMs systematically miscalibrate success probability and token usage on software-engineering tasks, causing auction outcomes to diverge from full-information allocation, and adding prior capability information only partly closes the gap.
medium descriptive arxiv
MarketBench: Evaluating AI Agents as Market Participants · Andrey Fradkin, Rohit Krishnan · Apr 26, 2026
Generative AI can trigger a sudden collapse in peer-review effort; when AI crosses a critical capability threshold, journals should loosen selectivity and invest in detection rather than tighten standards. Editors who continue to tighten risk amplifying wasteful author polishing without improving paper sorting.
n/a theoretical arxiv
Buying the Right to Monitor:Editorial Design in AI-Assisted Peer Review · Zaruhi Hakobyan · Apr 26, 2026
AI is remaking IT careers, not simply eliminating them: employers increasingly prize hybrid technical and higher-order skills, forcing a shift to reskilling, new role definitions and stronger governance; without firm investment in training and responsible deployment, benefits will be uneven.
medium review_meta semantic_scholar
The Impact of AI on Employability and Evolving Job Roles of IT Professionals · D. Gohel, Janak H. Maru · Fetched Apr 26, 2026
AI is reshaping tech workplaces but will widen or narrow gender gaps depending on how it is deployed; bias-aware algorithms, targeted reskilling and inclusive organisational design consistently improve women's participation and career progression.
medium review_meta semantic_scholar
Artificial Intelligence and GenderedEmployment: Reviewing Opportunities andChallenges for Women in Emerging TechnologySectors · Jay Patel · Fetched Apr 26, 2026
AI and robots are hollowing out routine low‑skilled roles and exerting downward wage pressure, even as augmentative AI boosts productivity for some — a dynamic that is widening inequality and spurring unions and governments to press for stronger workplace technology governance.
n/a review_meta semantic_scholar
From Technological Substitution to Institutional Response: A Systematic Review of Anxiety, Resistance, and Governance Transformation among Low Skilled Workers in the Age of Artificial Intelligence · Weixiang Gan, Mengfei Xiao, Tara Ahmed Mohammed, Qiuying Yue, Yongli Zhang · Fetched Apr 26, 2026
Existing legal and technical frameworks cannot reliably identify or hold autonomous AI agents accountable; five fundamental gaps — from intent verification to recursive delegation accountability — mean engineering work alone will not solve the problem. Foundational research on agent identity is urgently needed to enable safe cross‑organizational AI transactions.
n/a review_meta arxiv
AI Identity: Standards, Gaps, and Research Directions for AI Agents · Takumi Otsuka, Kentaroh Toyoda, Alex Leung · Apr 25, 2026
Racing to build domestic AI industries without embedding democratic economic governance risks consolidating corporate control of critical technologies and weakening democratic objectives. Policymakers should reorient AI industrial strategies toward competition, public purpose, and worker and community power to ensure AI serves broad social goals rather than narrow corporate interests.
n/a commentary semantic_scholar
Fighting for Democracy Amid the AI Race: Designing Tech Industrial Policy to Promote Democratic Economic Governance · Max von Thun, Audrey Stienon · Fetched Apr 25, 2026
Automated state systems boost administrative efficiency but erode democratic legitimacy: AI improves routine tasks and fiscal forecasting yet opaque algorithms (notably Australia’s Robo‑debt) produce procedural injustice. Without value-based transparency and role-sensitive explainability, the rise of algorithmic governance deepens an AI Core–Global South divide and risks long-term digital dependency.
medium review_meta semantic_scholar
Artificial Intelligence, Public Policy and Governance - implications for Economic Management and Political Systems · Glory Mmerechi Triumph Okereke, Philip Williams Appiah-Agyei · Fetched Apr 25, 2026
Pipeline-driven broadband expansion unlocked AI adoption in Turkey, with the biggest gains among small and software-heavy firms; increased connectivity not only raised AI use but also boosted productivity and exports while shifting employment toward ICT roles.
high quasi_experimental semantic_scholar
Digital Infrastructure, AI Adoption, and Firm Performance * · Nuriye Melisa Bilgin, G. Ottaviano · Fetched Apr 25, 2026
AI is already helpful for routine, data-heavy engineering tasks, but wider gains depend less on model intelligence than on plumbing: fragmented data, closed legacy toolchains, and verification and governance gaps block higher-order automation. Firms expect a staged progression from assistive tools to trustworthy agentic workflows as infrastructure, auditability, and organizational capabilities mature.
low descriptive semantic_scholar
Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities · Kristen M. Edwards, M. Bauer, Claire Jacquillat, A. J. Hart, Faez Ahmed · Fetched Apr 25, 2026
ByteDance used AI that learns across products and an adaptive organizational structure to scale and diversify at once, reversing the usual trade-off between specialization and expansion; diversification amplified the value of its AI assets rather than diluting them.
low descriptive semantic_scholar
Scaling high and wide: How firms leverage AI and organizational design to overcome the scale‐scope trade‐off · Feng Wan, Tianxi Yang, Xianwei Shi, Ke Rong, S. Ansari · Fetched Apr 25, 2026
Generative AI boosts management accounting by enriching data and sharpening analysis rather than substituting judgment; its benefits appear only when tied into standardized routines, integrated data infrastructures and strong cross‑functional governance, though evidence comes from public disclosures rather than internal causal proof.
low descriptive semantic_scholar
Research on the Impact of Generative AI on the Quality of Management Accounting Decisions: Evidence from Manufacturing Firms · Xintian Shen · Fetched Apr 25, 2026
Complex human-AI tasks can lower employee engagement by raising tech-learning anxiety, but confidence with AI and humble leaders blunt the harm.
low correlational semantic_scholar
How does human-AI collaboration task complexity affect employee work engagement? The roles of humble leadership and AI self-efficacy. · Boli Wang, Simeng Liu, Chenhao Luo · Fetched Apr 25, 2026
An AI-augmented visual knowledge-graph interface helps users build clearer conceptual structure: in a 12-person lab study, MindTrellis yielded higher expert ratings for coverage and structure and reduced cognitive load compared with retrieval-only tools. The result suggests that jointly constructed, editable knowledge representations can improve individual information synthesis in short-term tasks.
low quasi_experimental arxiv
MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration · Xiang Li, Cara Li, Emily Kuang, Can Liu, Jian Zhao · Apr 25, 2026
Generative AI can act as a cognitive collaborator—boosting idea generation, synthesis and strategic framing—but may also blunt intrinsic motivation and evaluative rigor, creating a 'Creativity Paradox' that firms must design around.
n/a theoretical openalex
Beyond the Creativity Paradox: A Theory-informed Framework for Role-based Integration of Generative AI in Organisational Creativity · Youngseok Choi, Chang Won Park, Ceyda Paydas Turan, Habin Lee · Apr 25, 2026
Industrial robots have only modest links to job quality across Europe once controls are included, but they reduce physical risks and appear to narrow the gender gap in autonomy while women still face higher work intensity.
medium correlational openalex
Gendered Effects of Robotisation on Job Quality · Dagmara Nikulin · Apr 25, 2026
Improved AI can reduce firm deployment when weak governance forces broader authority exposure, creating a 'deployment paradox' where capability gains fail to translate into productive use; stronger governance that cuts breach losses restores deployment, while externalities make social under-use more pervasive.
n/a theoretical arxiv
The Security Cost of Intelligence: AI Capability, Cyber Risk, and Deployment Paradox · Sukwoong Choi · Apr 24, 2026
AI coding agents often burn far more tokens than expected — input tokens drive costs and usage can vary 30x between runs; some models consume millions more tokens than others and none reliably predict their own token bill.
medium descriptive arxiv
How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks · Longju Bai, Zhemin Huang, Xingyao Wang, Jiao Sun, Rada Mihalcea, Erik Brynjolfss... · Apr 24, 2026
Shapley explanation variants raise analyst confidence without improving accuracy: in 3,735 fraud-case reviews, popular quantitative explanation proxies (sparsity, faithfulness) did not predict human-perceived clarity or decision utility, creating a risk of automation bias in operational decision systems.
medium quasi_experimental arxiv
Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings · Inês Oliveira e Silva, Sérgio Jesus, Iker Perez, Rita P. Ribeiro, Carlos Soares,... · Apr 24, 2026
Pretrained ‘foundation’ time‑series models beat bespoke forecasts across 54 energy datasets, with largest gains in covariate-rich and highly aggregated series. Accuracy improvements are robust across categories, correlate with spectral entropy, and show limited returns beyond a moderate context length.
medium descriptive arxiv
FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting · Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair · Apr 24, 2026