We live with access to more information than ever before and less guidance on what to do with it. People aren’t built for infinite choice. The skill that matters isn’t knowing where to find things. It’s knowing what’s worth your time. I call that skill taste.

Taste is what you allow in. It means being intentional about what you read, what ideas you sit with, what you choose to pass along.

In this Reading List series I’m using my taste to curate what I think is worth your time this week. Curation is care. It says: I thought about this, I chose it, I decided it was worth your time.

This week’s reading list includes three pieces on how technology reshapes human skill and attention: AI commoditizing code, how AI lets researchers bypass the hard work of scientific training, and software evolving from tools into systems designed to shape user behavior.

1. Code is cheap. Show me the talk.

Kailash Nadh
Published: 01/30/2026

** LLMs commoditize code, shifting developer value to articulation while warning AI reliance threatens junior training**
For decades, writing functional and reliable software was a high-effort, high-skill endeavor. Today, LLMs can instantly generate complex documentation, polished interfaces, and functional codebases, turning code from a scarce and expensive artifact into a cheap commodity.

For engineers with experience, this cuts development overhead and shifts the role toward architecture, critical thinking, and articulation. The risk falls on the next generation: easy-to-generate code may deprive them of the foundational grunt work that builds real intuition and judgment.

Filed Under: #opinion #softwareEngineering #generativeAi #laborEconomics #professionalIdentity

2. The machines are fine. I’m worried about us.

Minas Karamanis
Published: 03/30/2026

Automating the grunt work of research with AI risks hollowing out the intuition and training of future scientists
Graduate research exists to develop the scientist, not just produce results. Students using AI can match the publication metrics of those who learn manually, but they miss the intuition that comes from the hard work of debugging and deep analysis.

When junior researchers use AI to skip the thinking and implementation phases, they lose the failures and struggles that are the real curriculum. The sequence matters: tools built on expertise are productive. Used instead of training, they leave the user diminished.

Filed Under: #opinion #scientificResearch #generativeAi #higherEducation #skillAcquisition #learningStrategies

3. Backseat Software – Mike Swanson’s Blog

Mike Swanson
Published: 01/18/2026

Developer argues software has shifted from a utility tool to a behavioral engagement channel driven by telemetry and A/B testing
Modern software evolved from a tool you use into a channel that uses you. What started as analytics to improve stability gradually became a system of nudges, push notifications, and engagement metrics designed to prioritize company growth over user intent.

Mike Swanson argues for a return to intentional design: separate stability telemetry from growth metrics, treat user interruptions as opt-in, and focus on long-term trust over short-term return visits.

Filed Under: #opinion #userExperience #softwareEngineering #productDesign