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AI's productivity dividend is conditional: in Chinese listed firms, AI adoption substantially raises TFP for resource‑constrained firms but yields negligible or diminishing returns for technologically advanced firms in hypercompetitive markets, driven by automation efficiencies in the former and redundant AI/innovation stagnation in the latter.
medium correlational openalex
The Heterogeneous Effects of Artificial Intelligence on Enterprise Total Factor Productivity: Key Mechanisms and Strategic Implications · Xu Chu, Qingyu Han · May 01, 2026
Banks that integrate AI see measurable gains in profitability and governance efficiency — higher ROA, improved operational and risk-adjusted performance, and lower compliance costs — but algorithmic bias and employee displacement remain material risks.
medium quasi_experimental openalex
Research on the Transformation Acceleration of Financial Institutions and Governance Efficiency with Artificial Intelligence Technology · Guangyuan Han, Huiping Shang, Keying Li, Jingzhi Han · May 01, 2026
Synthetic 'computers' simulate month-long, user-specific work environments to train agents: 1,000 long-horizon runs (8+ hours, ~2,000 turns) produce richer experiential signals and improve agent performance on in- and out-of-domain productivity evaluations, though real-world validation is still pending.
low descriptive arxiv
Synthetic Computers at Scale for Long-Horizon Productivity Simulation · Tao Ge, Baolin Peng, Hao Cheng, Jianfeng Gao · Apr 30, 2026
A live benchmark shows state-of-the-art LLM agents complete at most two-thirds of realistic workflow tasks: the top model passes 66.7% of 105 controlled tasks. Failures cluster in HR, management and multi-system business workflows, indicating end-to-end workflow automation remains far from solved.
n/a descriptive arxiv
Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows · Chenxin Li, Zhengyang Tang, Huangxin Lin, Yunlong Lin, Shijue Huang, Shengyuan L... · Apr 30, 2026
Clinician overrides are valuable preference signals, not noise; jointly learning a reward model and clinician capability from longitudinal override and outcome data can align clinical AI to patient trajectories and prevent the systematic suppression of correct-but-difficult recommendations. Value-based chronic care—with dense longitudinal outcomes and natural capability variation—offers especially favorable conditions for this approach.
n/a theoretical arxiv
Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care · Prabhjot Singh, Abhishek Gupta, Chris Betz, Abe Flansburg, Brett Ives, Sudeep La... · Apr 30, 2026
Language models, when paired with symbolic abstractions and numerical optimisation, substantially improve simulated linkage design—cutting geometric error by up to 68% and markedly raising structural validity; however, evidence is limited to simulation and a small set of targets.
medium descriptive arxiv
Language Models Refine Mechanical Linkage Designs Through Symbolic Reflection and Modular Optimisation · João Pedro Gandarela, Thiago Rios, Stefan Menzel, André Freitas · Apr 30, 2026
Pictures and colours nudge vision-language models: exposure to ‘kind’ images and cooperative color cues increases model cooperation in an iterated Prisoner’s Dilemma, and prompt edits, Chain-of-Thought, or reducing visual tokens can lessen—but not uniformly eliminate—these biases across models.
medium quasi_experimental arxiv
The Effects of Visual Priming on Cooperative Behavior in Vision-Language Models · Kenneth J. K. Ong · Apr 30, 2026
Differentiable AI weather models can produce gradient-based payment signals that recover near-optimal sensor value and support monotonic rewards for participatory IoT networks; however, these signals are computationally vulnerable to adversarial input inflation and need external baselines for reliable detection.
medium descriptive arxiv
Calibrating Attribution Proxies for Reward Allocation in Participatory Weather Sensing · Mark C. Ballandies, Michael T. C. Chiu, Claudio J. Tessone · Apr 30, 2026
Schema-grounded 'xmemory' turns AI memory from fuzzy search into a verified system-of-record, lifting end-to-end F1 to 97.1% (versus 80–87% for retrieval baselines) and achieving 95.2% on an application task; the result suggests architecture and write-path verification matter more than retrieval scale or raw model strength for production memory workloads.
medium descriptive arxiv
From Unstructured Recall to Schema-Grounded Memory: Reliable AI Memory via Iterative, Schema-Aware Extraction · Alex Petrov, Alexander Gusak, Denis Mukha, Dima Korolev · Apr 30, 2026
Relaxing latency constraints can shift AI inference to cheaper, lower‑carbon regions, but real‑world frictions — from egress charges and capacity limits to regulatory and state‑locality rules — sharply cut the potential electricity and emissions gains.
low theoretical arxiv
AI Inference as Relocatable Electricity Demand: A Latency-Constrained Energy-Geography Framework · Xubin Luo, Yang Cheng · Apr 30, 2026
Generative AI overviews now appear for roughly half of web queries and reshape which sites users see, favoring Google-owned content and sidelining publishers that block Google's AI crawler; the AI summaries are also less stable and less robust to small query changes than traditional search results, raising concerns about publisher visibility and revenue.
medium descriptive arxiv
How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews · Riley Grossman, Songjiang Liu, Michael K. Chen, Mike Smith, Cristian Borcea, Yi ... · Apr 30, 2026
Current agent 'memory' is mostly lookup, not learning—this matters: without slow weight-based consolidation, agents cannot develop expertise, face a provable ceiling on generalizing to novel compositions, and remain structurally vulnerable to persistent poisoning; pairing fast exemplar storage with slower consolidation is required.
medium theoretical arxiv
Contextual Agentic Memory is a Memo, Not True Memory · Binyan Xu, Xilin Dai, Kehuan Zhang · Apr 30, 2026
Engineering teams with stronger AI capabilities report higher innovation performance because AI improves decision-making quality; making algorithms more transparent amplifies that benefit.
low correlational semantic_scholar
AI meets engineering ingenuity: how AI capability enhances innovation performance through decision-making quality · Ling Xiang, Fuda Li · Fetched Apr 30, 2026
AI threatens to dissolve firms' coordinating role by internalizing human interfaces into computation, shifting value from physical assets and human collaboration to continuous, tokenized data flows. Domain-specific data-refinement infrastructure and regionally governed data-sovereignty bodies, not traditional firms or markets, may become the primary coordinators of production.
n/a theoretical arxiv
Structural Dissolution: How Artificial Intelligence Dismantles Coordination Architecture and Reconfigures the Political Economy of Production · Chao Li, Chunyi Zhao · Apr 30, 2026
A five-point leadership spectrum — Pure Human, Centaur, Co‑equal, Minotaur and Pure AI — exposes how decision authority can quietly shift from people to algorithms, making oversight ceremonial or counterproductive; leaders need to spot configuration drift and cultivate 'co‑adaptability' to keep responsibility and capability aligned.
n/a theoretical arxiv
Leading Across the Spectrum of Human-AI Relationships: A Conceptual Framework for Increasingly Heterogeneous Teams · Alejandro R. Jadad · Apr 30, 2026
Many AI projects are abandoned for practical reasons, not just ethics: a review and practitioner data show funding shortfalls, development hurdles and internal dynamics often halt systems long before deployment. Responsible AI efforts should therefore engage upstream decisions that determine which systems ever get built, not only how deployed systems behave.
medium descriptive openalex
To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems · Shreya Chappidi, Jatinder Singh · Apr 30, 2026
Generative AI is reshaping how freelancers learn—used widely as an on-demand tutor but not trusted as the primary trainer; upskilling has shifted from long-term growth to immediate market survival, producing skills that are hard to verify in competitive freelance markets.
low descriptive arxiv
Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers · Kashif Imteyaz, Isabel Lopez, Nakul Rajpal, Hunjun Shin, Saiph Savage · Apr 29, 2026
An LLM-driven agent autonomously ran thousands of reasoning and lab actions to reproduce optics experiments and discover a previously unreported optical bilinear interaction; the validated mechanism hints at a path toward high-speed, energy-efficient optical hardware for pairwise computation.
medium descriptive arxiv
End-to-end autonomous scientific discovery on a real optical platform · Shuxing Yang, Fujia Chen, Rui Zhao, Junyao Wu, Yize Wang, Haiyao Luo, Ning Han, ... · Apr 29, 2026
Dependence on LLMs risks eroding engineers' ability to diagnose and fix complex systems, creating hidden 'epistemological debt' that can amplify outages like Amazon's 2026 failure; firms should pair AI tools with rigorous human-in-the-loop training and governance to preserve system resilience.
low theoretical arxiv
Cognitive Atrophy and Systemic Collapse in AI-Dependent Software Engineering · Frank Ginac · Apr 29, 2026
Generative AI is reshaping hiring from behind the scenes: recruiters retain formal authority but genAI quietly structures job definitions and evaluation inputs, producing only small productivity gains while accelerating deskilling and reducing meaningful oversight.
low descriptive arxiv
Resume-ing Control: (Mis)Perceptions of Agency Around GenAI Use in Recruiting Workflows · Sajel Surati, Rosanna Bellini, Emily Black · Apr 29, 2026
Generative AI reshuffles the make-or-buy ledger but stops short of a SaaSocalypse: firms will increasingly build commodity utilities and AI-differentiating applications in-house, yet regulated and mission-critical systems are still likely to be purchased from vendors.
n/a theoretical arxiv
The Buy-or-Build Decision, Revisited: How Agentic AI Changes the Economics of Enterprise Software · David Klotz · Apr 29, 2026
Emerging evidence suggests large language models reshape jobs unevenly: some occupations face substantial task-level exposure while real-world displacement remains limited and concentrated; firms that invest in transparent planning, reskilling and role redesign can both mitigate harm and capture productivity gains.
medium review_meta semantic_scholar
AI Displacement Risk in the Labor Market: Evidence, Exposure, and the Imperative for Adaptive Organizational Strategy · Jonathan H. Westover · Fetched Apr 29, 2026
Early adopters of generative AI in 2022–25 have reorganized knowledge work: firms that integrated AI tools show significant occupational reallocation, task realignment and shifts in pay dynamics compared with matched non-adopters.
medium quasi_experimental semantic_scholar
The Generative AI Revolution: Early Evidence of Structural Transformation in U.S. Workplace Hierarchies, Job Roles, and Labor Market Dynamics · A. O'Connor · Fetched Apr 29, 2026
Adopting GitHub Copilot is linked to modestly higher software-engineer hiring—especially junior hires—and to new hires with slightly more non-coding skills, without evidence of reduced coding ability.
low correlational semantic_scholar
Firms' GitHub Copilot adoption and labor market outcomes for software engineers · Matthew Baird, M. Carpanelli, Brian Xu, Kevin Xu · Fetched Apr 29, 2026
AI is reshaping Israel's labor market — slowing hiring and reshuffling jobs rather than triggering mass layoffs — while a patchwork legal framework leaves gaps on transparency, accountability and workers' rights.
low descriptive semantic_scholar
Artificial Intelligence in Israel, Trends, Developments, and Implications for Employment and Labor Policy · S. Yanisky-Ravid · Fetched Apr 29, 2026