Deep research requires a slower pace than tech industry work

Anyone working in an industry for a while will become accustomed to that culture—its processes, its norms, its values, its tacit knowledge. Much of this is incredibly valuable, of course, but these ideas can also represent constraints. There are some important impedances here between tech industry culture and research culture. In particular, tech culture is calibrated to a much faster pace. This can lead to impatience or early abandonment when confronting problems which require a researcher’s pace.

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

The machines are fine. I'm worried about us.

On AI agents, grunt work, and the part of science that isn't replaceable.

Agentic Coding is a Trap | Lars Faye

Remaining vigilant about cognitive debt and atrophy.

The case for friction in AI-mediated information seeking and learning

Introduction. This paper challenges the assumption that frictionless AI-mediated information seeking represents progress. We argue that AI systems eliminating productive friction, such as uncertainty, exploration, and reflective processes, undermine intellectual virtues essential for critical thinking in an AI-saturated information environment. Method. This conceptual article employs a theory synthesis approach, drawing on library information science (LIS) theories of information behaviour, experience, and literacy, virtue epistemology, and human–computer interaction (HCI) friction design literature. Analysis. We map intellectual virtues such as curiosity, thoroughness, and intellectual humility onto the Association of College and Research Libraries (ACRL) Framework for Information Literacy for Higher Education, building on and extending previous analyses. We connect dimensions of virtuous search to AI system design principles and align friction types with specific intellectual virtues. Results. We propose three design principles for human-centred AI systems (representation, affordance, and facilitation) and develop a typology that maps friction interventions to intellectual virtues, providing concrete examples for each. Conclusion. Productive friction should be understood as a feature supporting intellectual development, not a barrier to efficiency. The design choices made today will determine whether AI serves as intellectual scaffolding or cognitive crutch.