Women Talkin' 'Bout Friction

Devon Cantwell-Chavez studies global urban climate change governance. She and Kimberly met because of an antagonistic LinkedIn post (not between the two of them), and then discovered that, in many ways, they came up the same way. They both were Teach for America corps members, both early believers in classroom technology, and both landed somewhere far more critical. This conversation is about what gets lost when we design friction out of learning and research. 

They get into the myth of the digital native and why "nobody knows how file folders work anymore," the frictionless interfaces that train us to just ask instead of think, and Goodhart's Law, the idea that a measure stops being effective once it becomes a target. Devon lays out how her research team built friction back in on purpose with a no-first-use policy, low-stakes-only translation tools, and a tagging system so every use of AI is on the record. 

The episode closes on why AI can't be replicated the way rules-based software can, what that means for qualitative research, and where Devon finds hope, on the lawns of rural Michigan, in t-shirts and yard signs against data centers.

Mentioned in this episode:

• The AI Con, Emily Bender and Alex Hanna 
• Being Wrong: Adventures in the Margin of Error, Kathryn Schulz 
• Right Kind of Wrong: The Science of Failing Well, Amy Edmondson
• Kimberly's Substack that discusses the following: 
  • Don Norman on design responsibility 
  • Rosina Lippi-Green on communication as a two-way street
• Goodhart's Law
• Devon's viral LinkedIn post 
• The chess-cheating study (in understandable language) or the research manuscript preprint
• The Brown University exam experiment 
• Box Elder County data center coverage 
• Digital Natives
• Non-Consensual Sexual Imagery
• Devon's t-shirt
• Cancellation of data center projects: https://www.datacenterwatch.org/report

Fact Checking Notes:

Traditional spell checkers were primarily dictionary- and rule-based, later augmented with statistical language models and machine learning. Modern writing assistants (including current Grammarly features) increasingly combine traditional spelling and grammar checking with large language models.