AI & cognitive deskilling

AI deskilling is a structural problem

Many artificial intelligence tools replace or stand to replace human activity, via automated decision-making, recommender systems and content generation. The more artificial intelligence (AI) replaces valuable human activity, the more it risks deskilling humans of their human capacities. This paper argues for applying a structural perspective to this phenomenon. It introduces the concept of ‘capacity-hostile environments’ to identify instances where AI mediation impedes human capacity cultivation. The analysis moves beyond individual responsibility that agents have to cultivate their human capacities, demonstrating how AI’s influence creates systemic conditions that could inhibit the development and exercise of human capacities by undermining the process of capacity cultivation. Drawing on the philosophy of skill as well as social epistemology, this paper argues that capacity cultivation (skilling) includes acquiring agential control over the capacities, inculcated through a long, gradual process of habituation. Habituation, in turn, depends on learning from others: the ‘know how’ of the skill, as well as a shared understanding of the value of the skill. AI mediation risks undermining the quality of the conditions for capacity habituation, leading to capacity impoverishment. By exploring the role of AI in mediating human activity, the paper highlights the need to evaluate AI applications based on their conduciveness or hostility to capacity cultivation. Ultimately, it calls for a critical reflection on the values inherent in AI socio-technical systems and emphasizes the societal obligation to foster capacity-conducive environments in the age of AI.

Large-Language Models as a Cognitive Virus

Large-language models (LLMs) are rapidly becoming part of human culture, reshaping how information is produced, transmitted, and used. Here we propose that their diffusion can be understood through a viral analogy, with LLM use spreading through populations, becoming embedded in cognitive and cultural practices. We model transitions among uncoupled, coupled, and persistently dependent users, and show that the interplay between social transmission, recovery, and collective reinforcement can generate tipping points and technological lock-in. A central consequence is the possibility of runaway dynamics: once a critical threshold is crossed, small increases in adoption can trigger rapid population-level shifts toward persistent dependence, with abrupt losses in cognitive competence. The same framework, however, identifies conditions for cognitive immunization, based on reducing transmission and facilitating reversibility. Our results highlight how LLM adoption may involve nonlinear collective transitions with important consequences for cognitive autonomy.

Illusions of Understanding from Outsourcing Thinking to LLMs

Some illusions of understanding are an inevitable part of the research process, while others can be avoided or overcome by careful critical thinking and observation. We are facing an increased risk of avoidable illusions as more research activities are delegated to large language models (LMM). LLMs can be useful but they cannot think, and their use can undermine our thinking and understanding. Thinking for ourselves is hard and error prone but worthwhile - and there are no shortcuts to understanding.

Agentic Coding is a Trap | Lars Faye

Remaining vigilant about cognitive debt and atrophy.

How AI Impacts Skill Formation

AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.

The End of Reading Is Here

Optimists once believed that universal literacy was inevitable. Now it seems that the age of reading might be a short anomaly in human history.

Itai Yanai on Twitter / X

Unpopular opinion: Going straight to AI limits your creatively, because it short-circuits the iterative process you need to develop new ideas. pic.twitter.com/O2iPs0bfh0— Itai Yanai (@ItaiYanai) June 20, 2026

We May Be Entering A Second Axial Age

The transition from small hunter-gatherer societies into complex civilizations gave rise to the first Axial Age. Today, the planetary polycrisis of climate chaos, mass migration, increasing warfare and transformative AI represents a rupture of comparable magnitude.

Technology is a Siren Song

Orangutan thinking This started with a question I asked myself: How do our minds change when we can receive a plausible, informed, and convincing answe...

God Damn AI is making me dumb

It's so god damn tempting to use AI to write. Whether it is articles, code, or documents. I feel like using AI is diminishing my ability to write myself. ...

The Impact of Artificial Intelligence on Human Thought

This research paper examines, from a multidimensional perspective (cognitive, social, ethical, and philosophical), how AI is transforming human thought. It highlights a cognitive offloading effect: the externalization of mental functions to AI can reduce intellectual engagement and weaken critical thinking. On the social level, algorithmic personalization creates filter bubbles that limit the diversity of opinions and can lead to the homogenization of thought and polarization. This research also describes the mechanisms of algorithmic manipulation (exploitation of cognitive biases, automated disinformation, etc.) that amplify AI's power of influence. Finally, the question of potential artificial consciousness is discussed, along with its ethical implications. The report as a whole underscores the risks that AI poses to human intellectual autonomy and creativity, while proposing avenues (education, transparency, governance) to align AI development with the interests of humanity.

AI Assistance Reduces Persistence and Hurts Independent Performance

People often optimize for long-term goals in collaboration: A mentor or companion doesn't just answer questions, but also scaffolds learning, tracks progress, and prioritizes the other person's growth over immediate results. In contrast, current AI systems are fundamentally short-sighted collaborators - optimized for providing instant and complete responses, without ever saying no (unless for safety reasons). What are the consequences of this dynamic? Here, through a series of randomized controlled trials on human-AI interactions (N = 1,222), we provide causal evidence for two key consequences of AI assistance: reduced persistence and impairment of unassisted performance. Across a variety of tasks, including mathematical reasoning and reading comprehension, we find that although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up. Notably, these effects emerge after only brief interactions with AI (approximately 10 minutes). These findings are particularly concerning because persistence is foundational to skill acquisition and is one of the strongest predictors of long-term learning. We posit that persistence is reduced because AI conditions people to expect immediate answers, thereby denying them the experience of working through challenges on their own. These results suggest the need for AI model development to prioritize scaffolding long-term competence alongside immediate task completion.

The homogenizing effect of large language models on human expression and thought

AbstractCognitive diversity, reflected in variations of language, perspective, and reasoning, is essential to creativity and collective intelligence. This diversity is rich and grounded in culture, history, and individual experience. Yet, as large language models (LLMs) become deeply embedded in people's lives, they risk standardizing language and reasoning. We synthesize evidence across linguistics, psychology, cognitive science, and computer science to show how LLMs reflect and reinforce dominant styles while marginalizing alternative voices and reasoning strategies. We examine how their design and widespread use contribute to this effect by mirroring patterns in their training data and amplifying convergence as all people increasingly rely on the same models across contexts. Unchecked, this homogenization risks flattening the cognitive landscapes that drive collective intelligence and adaptability.

How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt

The term technical debt is often used to refer to the accumulation of design or implementation choices that later make the software harder and more costly to understand, modify, or extend over time...

The risks of AI in schools outweigh the benefits, report says

A new report warns that AI poses a serious threat to children's cognitive development and emotional well-being.

The Coming AI Cataclysm | Compact

In the decade since the neural architecture version of Google Translate and the invention of transformer architecture for neural networks, we have experienced the most rapid technological breakthroughs since at least World War II, and possibly ever.

A World Unobserved

Humans have always generated knowledge and judged it. That's changing fast.

AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking

The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists.