🌱 Cognitive capacity
Objective
Understand the limits of human cognitive capacity and how digital environments interact with those limits — either exploiting them or designing around them. Explore how AI might assist with information filtering and decision-making, and what conditions help people learn and grow without burning out.
Why this topic
Left social media due to overwhelm, but the parallel problem is the opposite of noise — there is too much worth learning, reading, and listening to, with not enough time or mental bandwidth. Both problems point to the same underlying question: how do we choose where to focus when capacity is finite?
Working thesis
Cognitive overload is both an evolutionary mismatch and a design failure. Human brains were not built for this volume of information or decisions, and digital products compound the problem by designing as though those limits don't exist. Good design should treat cognitive capacity as a constraint to work within, not a threshold to push against. AI has genuine potential here — not to think for us, but to filter, prioritise, and reduce decision surface so human attention goes where it matters.
Current stage
Fresh. No prior reading.
Key tensions
Using AI to reduce cognitive load risks the same agency erosion described in post-attention economy — if AI decides what is worth your attention, who is optimising for what? Reducing cognitive load and maintaining intellectual autonomy may be in direct tension. Also: some cognitive friction is generative — difficulty and struggle are how learning happens. Designing it out entirely produces ease without growth.
Open questions
- What are the actual documented limits of human cognitive capacity?
- How can AI assist with information filtering without becoming another influence architecture layer?
- What conditions best support learning and intellectual growth at a societal level?
- How do we develop better frameworks for choosing where to focus?