Starting Point
AI can make a weak claim feel polished. That is useful only if the workflow also makes uncertainty more visible.
Main Idea
An AI research workflow should pressure-test reasoning rather than replace reading. It should help expose competing explanations, missing controls, and places where language has become smoother than the evidence.
What I Agree With
AI can summarize, compare, and generate alternative framings quickly. Used carefully, it can help identify what evidence would change a conclusion.
What I Question
I question workflows that use AI mainly to produce confidence. Scientific judgment depends on knowing when a claim remains weak.
Current Model
The useful role of AI is adversarial and reflective: extract competing explanations, ask what would falsify the current view, then compare the model’s summary with the actual paper.
Connections
- Mechanism-first vs Prediction-first Science
- Research notes as working memory
- Future thinking notes on scientific explanation
Open Questions
- What parts of reading should remain slow?
- How can AI make uncertainty visible instead of hiding it?
- When does summarization become premature closure?
Revision History
- 2026-07-07: Converted into the Thinking Note structure.