Assorted links: dashboards, LLM, IA, bitcoin

The World’s Digital Leaders in 2026

Source: Visual Capitalist

I. Understanding the AI That Drives Robots

An Explanation of Vision-Language-Action Models (Construction Physics).

II. Don’t let your AI dashboard drive your corporate strategy

AI-powered dashboards are pitched as the ultimate tool for chief executives looking for strategic agility. In practice they are crippling corporate leadership and undermining middle-management. The author argues that to lead effectively in the algorithm-driven enterprise, executive leadership must abandon the illusion of omniscient control and embrace curated latency (LSE Business Review).

III. Artificial intelligence and climate change: balancing innovation and sustainability

Artificial intelligence (AI) is becoming an increasingly important factor in the climate transition. AI can improve energy efficiency, optimise resource use, accelerate climate innovation and strengthen climate forecasting, adaptation and risk management. At the same time, the development and deployment of AI require substantial computational resources, increasing electricity demand and, depending on the carbon intensity and timing of electricity use, associated emissions. We examine AI’s direct environmental footprint, its contribution to climate mitigation and adaptation, and rebound effects arising from lower costs, higher productivity and wider adoption. The overall impact of AI on climate change also depends on how AI capabilities evolve. We consider two stylised scenarios. Under an AI copilot scenario, benefits and environmental costs are likely to build up more gradually, but the net climate effect remains uncertain, depending on whether efficiency gains outpace growth in AI use, on the carbon intensity of additional electricity needed to power AI, and on how productivity gains translate into economic activity and resource use. Under a more transformative scenario involving artificial general intelligence (AGI), with cognitive capabilities comparable to those of humans across a broad range of tasks, both the potential benefits and environmental risks could become larger and more uncertain. The paper also reviews emerging policy responses and highlights the relevance of the AI–climate nexus for central banks. By affecting productivity, energy systems and climate-related risks, the AI–climate nexus can influence potential output and inflation dynamics, while the scale and financing of investment in AI and energy infrastructure may also have implications for financial stability (BIS).

IV. Geoeconomic pressure

The increasing use of geoeconomic pressure is reshaping the global economy. We introduce a methodology using large language models (LLMs) to systematically measure the application of and response to geoeconomic pressure from large textual corpora, classifying which governments apply pressure to which foreign targets, using which in struments, firms, and products. Most importantly, we use LLMs to analyze how and why firms respond to the pressure. We focus on export controls, sanctions, and tariffs as the main instruments of pressure on firms. We find that export controls induce the targeted firms to undertake domestic R&D to build a domestic alternative technology. Western sanctions on Russia cause supply chain re-arrangements, the major beneficiary of which are firms in third-party countries, like China and India, which step into the market vacuum created by the broken business relationships. The threat and impositions of U.S. tariffs cause losses for the majority of U.S. firms, which report absorbing the cost shock on their inputs via their profit margin. Foreign firms are also negatively affected, mostly via the related macroeconomic uncertainty and the cost of relocating production (BIS).

V. Why Users Stay: Bridging Adoption and Continuance in Bitcoin Use through a Mixed-Methods Approach

This study examines how blockchain attributes, individual factors, and institutional conditions shape users’ intention to adopt Bitcoin and influence post-adoption outcomes. We followed a mixed-methods approach and collected the data from January–July 2025. The qualitative phase included in-depth semi-structured interviews with experienced Bitcoin users and a thematic analysis to identify key motivations and barriers to adoption. The quantitative phase included a pre-tested survey, and 268 Bitcoin users responded. We used partial least squares structural equation modeling (PLS-SEM) to validate the conceptual model. Major findings suggest that blockchain transparency, information security, relative advantage, and financial self-sufficiency significantly and positively affect the intention to adopt Bitcoin, whereas government support policy has a negative influence. Additionally, adoption intention strongly predicts post-adoption satisfaction, willingness to use, and perceived system quality. By integrating pre-adoption and post-adoption determinants grounded in expectation-confirmation theory (ECT), this research offers a more comprehensive understanding of Bitcoin user behavior. From a practical perspective, the results provide valuable insights for policymakers, developers, and financial service providers seeking to enhance user trust and promote responsible adoption and long-term usage (International Journal of Innovation Studies).