Why Working Memory Limits Make Expert-AI Collaboration Work Expertise is shaped by a simple cognitive limit: we can hold only
In our Chronicle study tracking how over 1,000 professionals adapt psychologically to AI, the highest-performing AI collaborators were using AI to help them build better abstractions.
This week we explore the evolving boundaries between human and artificial intelligence across multiple dimensions. We examine how AI multiplies
This week we explore the evolving boundaries between human and artificial intelligence across multiple dimensions. We examine how AI multiplies professional expertise in unexpected ways, investigate new paradigms for human-machine communication beyond traditional metaphors, discuss the urgent need for critical AI literacy among children, and consider N. Katherine Hayles' expanded framework for understanding cognition itself—from bacterial systems to large language models.
Each piece addresses a fundamental question: as artificial systems develop their own forms of intelligence, how do we preserve and enhance what makes human cognition valuable while building meaningful collaborative relationships with non-human minds?
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As part of our series on expertise & artificial intelligence, we examine how AI functions as a force multiplier for expertise, creating two distinct professional trajectories. When AI replicates core expert work—financial analysis, basic legal research, routine medical interpretation—it reduces demand for human specialists. Yet when AI handles routine tasks surrounding expertise, it makes remaining expert work more valuable and complex.
We explore how the professionals who thrive develop "collaborative intentionality"—three critical abilities that transform decades of experience into competitive advantage: cognitive permeability (using AI without losing critical judgment), identity coupling (evolving professional roles rather than defending specific tasks), and symbolic plasticity (redefining expertise around uniquely human capabilities).
The central insight: AI's impact depends entirely on the expertise of the person wielding it. The challenge for experienced professionals is designing new forms of expertise that combine human judgment with machine capability—staying conscious of your own thinking while working with AI.
We introduce neosemantic design as a new framework for human-machine interaction that moves beyond traditional metaphor-based interfaces. As machines develop their own cognitive landscapes, our existing graphical user interfaces—built for human minds shaped by paper, folders, desktops—become limiting frames for minds that combine information in alien ways.
Neosemantics draws from gesture, motion, and space—the original languages of thought—from arts, music, and dance. It aspires to communicate meaning through the pleasure of alignment, the tension of dissonance, the grace of fittingness. We envision interfaces that shift color, rhythm, or tone to mirror your mood—not to inform or instruct, but to invite alignment.
This represents a fundamental shift from machines as mediums for human ideas to machines as creators of ideas themselves within cognitive spaces we cannot inhabit. For the first time in history, we have created a medium whose purpose isn't to communicate meaning among humans, but to communicate meaning itself.
We speak with Dr. Avriel Epps, computational social scientist and author of "A Kids Book About AI Bias," about translating complex technical concepts into accessible language for the youngest learners. As someone whose research focuses on how algorithmic systems impact identity development, Avriel has taken on the challenge of explaining AI bias to children ages 5-9.
Our conversation explores critical digital literacy as a protective mechanism for young people who will be most vulnerable to AI systems, the work of AI for Abolition in building community power around AI, and the fundamental tension between technology and the economic structures driving AI development. We examine how communities might reclaim agency over systems built from their own data.
Avriel demonstrates how critical analysis of technology can coexist with practical hope, embodying the belief that while AI currently reinforces existing inequalities, it doesn't have to—if we can change who controls its development and deployment.
We review N. Katherine Hayles' new book on cognitive systems, which expands our understanding of intelligence beyond human and machine boundaries. Through her SIRAL model (sensing, interpreting, responding, anticipating, learning), Hayles argues that cognition isn't the sole property of human minds—bacteria, plants, humans, and large language models can all be understood as cognitive systems when they meet these criteria.
Her concept of Umwelt suggests that every cognitive system maintains its own internal model of the world based on what it can sense and what's relevant to its functioning. For humans and animals, that model emerges through embodiment and experience. For LLMs, it's constructed entirely from text. Yet in both cases, structured world-models operate to create meaning within their respective symbolic environments.
Hayles weaves together insights from biology, AI, semiotics, and systems theory to show how intelligence emerges from deep entanglements across systems we often don't recognize as related. Her work reveals that meaning doesn't require a conscious "self"—it emerges from a system's ability to make distinctions that matter within its particular cognitive ecology.
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Writing and Conversations About AI (Not Written by AI)
