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Jeff Bezos argues that AI data centers should be prioritized for water and energy resources over human consumption to enable the development of superintelligence, sparking controversy over AI's environmental impact.
This paper introduces CEO-Bench, a multi-agent benchmark for evaluating LLMs on CEO-level strategic resource reallocation, revealing systematic failure modes and a structural integration–boldness tradeoff.
STARIXNet is a lightweight neural network that improves cloud resource allocation by capturing multivariate spatio-temporal relationships among system metrics, prioritizing service stability over forecast accuracy. Deployed at Walmart, it achieved 10-50% cost savings while maintaining service reliability.
This paper analyzes tradeoffs between latency, reliability, and cost in LLM-enabled agentic workflows, introducing performance models and deriving optimal resource allocation policies like water-filling token allocation.
This paper introduces Computable Fair Division (CFD), a framework using Boltzmann-Softmax control to balance efficiency and fairness in AI resource allocation, with real-time adaptation via AHC++.
This paper introduces CAST, a polynomial-time approximation algorithm for strategically allocating HIV treatment resources to virally unsuppressed individuals in a transmission network to minimize new infections, outperforming existing baselines on real-world networks.
This paper proposes an Edge-AI-driven decentralized task allocation framework for circular smart manufacturing that uses learning-to-rank to align with the ordering-based nature of winner selection. Simulation results show improved delay, deadline adherence, and energy efficiency under high-load and tight-deadline scenarios.
The article discusses the emerging trend of token budgeting in enterprises, highlighting the need for new management tools as AI agents consume significant compute resources. It suggests this will create a startup opportunity for software solutions that provide visibility and control over agentic spend.