Too Much to Read: Finding Clarity

Source: Sydney Sullivan Substack
Author: Sydney Sullivan
Original source: https://sydneysullivanphd.substack.com/p/too-much-to-read-finding-clarity Source type: essay

Private backup: the full article text is archived in the private repository at archives/articles/sydneysullivanphd-substack-com-too-much-to-read-finding-clarity.source.md. It is not published on the public Quartz site.

Summary

Sydney Sullivan compares today’s AI discourse to walking into a vast library and realizing that reading everything is impossible. The article offers a filtering strategy for educators overwhelmed by AI writing: prefer clarity over hype, check the source’s incentives, prioritize synthesis over novelty, follow trusted curators, and accept that missing things is inevitable. The core message is that staying informed does not require exhaustive consumption; it requires intentional attention and better filters. For educators integrating AI into pedagogy, Sullivan recommends a manageable reading rhythm that mixes peer-reviewed and opinion-based sources.

Pull quotes

Filter instead of drowning

“The reality is—just as in a real library—you can’t read it all. You shouldn’t even try. The trick is learning to filter.”

Synthesis over novelty

“Not every “new” update is worth chasing. What matters are the pieces that connect the dots—the writers who step back, compare findings, highlight patterns, and make sense of the bigger picture.”

Permission to miss things

“With AI, too, it’s okay to let things go by. If it’s truly important, it will resurface.”

Big ideas

Claims

Key evidence and examples

  • The article uses a library metaphor to emphasize that no educator can read everything being written about AI.
  • Recommended filters include skipping extreme headlines, asking who benefits, valuing synthesis over novelty, and following trusted curators.
  • Sullivan suggests a sustainable rhythm of three to five articles per week, mixing peer-reviewed and opinion-based sources.

Education relevance

Useful for teacher learning, professional development, faculty reading groups, AI literacy communities, and reducing AI-discourse overwhelm.

Durability note

Durability: High. The feed will change, but the practical need for filters, trusted curators, synthesis, and permission to miss things will remain central to AI-era professional learning.

My notes