Thinking
through AI
Abdication
Let AI think for you.
Accept the outputs uncritically.
Efficient — and epistemically hollow.
Refuse to engage.
Wait it out.
Safe — and increasingly untenable.
Abnegation
There is a path between.
Abdication
Let AI think for you.
Accept the outputs uncritically.
Efficient — and epistemically hollow.
Refuse to engage.
Wait it out.
Safe — and increasingly untenable.
Abnegation
There is a path between.
Three principles
AI is a medium for developing understanding — not a machine that automates it for you.
The skill is in how you structure the interaction — not which button you press.
When the output disappoints, examine your input first.
We'll come back to them.
Three principles
AI is a medium for developing understanding — not a machine that automates it for you.
The skill is in how you structure the interaction — not which button you press.
When the output disappoints, examine your input first.
We'll come back to them.
Three principles
AI is a medium for developing understanding — not a machine that automates it for you.
The skill is in how you structure the interaction — not which button you press.
When the output disappoints, examine your input first.
We'll come back to them.
When management researchers run audits, evaluations, or experiments on LLMs — what methods do they use, and what kinds of claims do they make?
We'll use AI to interrogate how our field engages with AI.
The reflexive move is the point.
When management researchers run audits, evaluations, or experiments on LLMs — what methods do they use, and what kinds of claims do they make?
We'll use AI to interrogate how our field engages with AI.
The reflexive move is the point.
When management researchers run audits, evaluations, or experiments on LLMs — what methods do they use, and what kinds of claims do they make?
We'll use AI to interrogate how our field engages with AI.
The reflexive move is the point.
When management researchers run audits, evaluations, or experiments on LLMs — what methods do they use, and what kinds of claims do they make?
We'll use AI to interrogate how our field engages with AI.
The reflexive move is the point.
The Socratic
conversation
Thinking with AI as a colleague.
"When management researchers run audits, evaluations, or experiments on LLMs, what methods do they use and what kinds of claims do they make based on those methods?"
Thinking through
The act of writing this prompt forced me to know what I was actually looking for.
The clarity achieved through the prompt is the insight.
"Here are five dominant approaches: behavioral audits, decision-support deployment studies, ethnographic accounts, survey-based attitudes, and case studies of adoption…"
Competent.
Useful.
Don't accept it, yet.
Step back.
Read what just happened.
Q1 → five canonical methods. Competent. Flat.
"Read this conversation. Why isn't it getting at what I'm actually trying to understand?"
The inward lens
Don't argue with the answer.
Bring the transcript into a new conversation, and ask the conversation what your prompt was carrying.
What my prompt
didn't carry.
Methods researchers use on LLMs — audits, evaluations, experiments.
A literature about using LLMs in research.
How researchers treat LLMs as theoretical objects — evaluative cognition, compliance modes, fairness reasoning.
A literature about what LLMs are like.
Studies make claims about what LLMs are like — but the methods often can't license those claims.
The construct-method matching gap.
The conversation didn't fail. The frame did.
"When management researchers treat LLMs as theoretical objects of study — making claims about what LLMs are like as evaluators, decision-makers, reasoners, or cognitive entities — what evidence do they use to support those claims?"
The inward lens
Resist the instinct to blame the model (e.g., not smart, biased).
Observe whether the output mirrored the input.
Notice what the frame was.
Three reads. One pattern.
Still not done.
- Four research postures (evaluators, decision-makers, silicon samples, psychological entities)
- Behavioral evidence dominates — outputs matching outputs
- Critical countercurrent surfacing (Lin 2025; dual-validity; Lindebaum & Fleming)
- Why do researchers settle on one account of LLM internals over others?
- What work is borrowed theoretical vocabulary doing?
- The map is here. The mechanism isn't. → Configuration 02
The transcript is
the work.
- Articulation of what you actually want.
- Conversational steering, diagnostic turns, reframing.
- Reflexive reversal: input before model.
And what stays hidden.
-
Verifying the underlying claims.Requires going back to the actual paper.
The conversation surfaces names; verification is still your reading. -
Systematic coverage of a literature.Mapping with criteria: journals, keywords, inclusion rules.
The same discipline scholars have always used; conversational AI doesn't replace it. -
Cross-session continuity.Solved by collaborative memoing at the end of a session.
A standalone artifact that can re-prime any model, any chat.
The orchestrated
search
Let agents discover. Then interrogate.
"Survey how management researchers treat LLMs as cognitive, evaluative, or psychological entities (2022–2026) — beyond using LLMs as tools."
What's being claimed about LLM internals — cognitive, evaluative, motivational, architectural?
Output patterns, human comparisons, behavioral paradigms, psychometric administration.
Where vocabulary borrows from psychology, behavioral economics, organizational theory.
Lin's Six Fallacies, dual-validity, Lindebaum & Fleming — what these critiques target.
AI alignment, interpretability, computational social science, HCI — where do management's standards diverge?
Where the same evidence supports multiple accounts. What work imported vocabulary does that the evidence can't.
Engagement design
A deep-research query is a contract.
Be precise about scope, structure, and what you want surfaced — let the agent split the work.
A query is a
methodological
decision
More to do.
More to ask.
"Survey how management researchers treat LLMs as cognitive, evaluative, or psychological entities (2022–2026) — beyond using LLMs as tools…"
Design the workflow yourself — phases, sub-agents, parallelize where useful.
Redirect if a stream is thinner or richer than expected.
Document what you did and what you skipped.
A folder, not a chat.
/papers— PDFs, Zotero-ready filenames..bib+.ris— bibliography.stream_[N]_*.md— per-stream summaries.synthesis.md— cross-stream, the analytical core.phase_[N]_notes.md— phase-by-phase reasoning.README.md— decisions, skips, caveats.
Quality bar — foundation for a published lit review. Inferential clarity over coverage breadth.
Engagement design
Be precise about what artifacts you expect — that is our half of the contract.
Be charitable about how the agent orchestrates — it knows its own runtime; let it split the work.
Stay clear on the analytical stages and outputs you want surfaced along the way.
"Most people stop here.
They cite the report and move on.
That's abdication
wearing a lab coat."
The report is raw material.
- Broad coverage across journals and years.
- Surfaced papers I wouldn't have found on my own.
- Clean structuring of method families.
Take it somewhere new.
-
New session, same model.Open a fresh session. Hand the report in — not as answer, as evidence.
The new session has no allegiance to its first take. -
Cross-agent.Feed Kimi's report into Claude. Feed ChatGPT's into Kimi.
Each model's frame surfaces the others' assumptions.
We'll walk path one in a moment. Path two is yours to take home.
"Here are deep research reports on how management research treats LLMs as cognitive entities.
Read them critically.
What assumptions do they carry?
What would a qualitative researcher notice that's missing?
What would a critical theorist push back on?"
A new session,
a different epistemic position.
Two modes.
Exploration.
Open-ended. Breadth-first.
The agent is your thinking partner.
"Let's read across the report. Where is it most coherent? Where does it strain?"
Engagement design
First, the open read.
The agent is your thinking partner — not your interrogator.
Two modes.
Exploration.
Open-ended. Breadth-first.
The agent is your thinking partner.
"Let's read across the report. Where is it most coherent? Where does it strain?"
Critical.
Step out of the field.
With another field's eyes — charitably both ways.
"Let's read this with AI-research eyes. Where does management's vocabulary diverge from how AI labs frame the same phenomena — and where would a management researcher reasonably push back?"
Not "you are X." Substantive perspective-taking — both sides have legitimate ground.
The difference is what you can't get from either alone.
Engagement design
Then, the critical read.
Collaborative framing — "let's read this as Y" — keeps the agent honest and keeps you in the loop.
Discovery and
interrogation, as a designed sequence.
- Engagement design across multiple sessions.
- Productive suspicion of any single output.
- A workflow with phases, each with its own purpose.
And what stays opaque.
-
Breadth comes at the cost of depth.The agent often works from titles, abstracts, and secondary write-ups.
When PDFs are accessible, it reads them, but you have to notice which is which. -
A gap between your query and the report.You can only channel so much understanding into a query.
Reading the report against your question is where the missing pieces show up. -
It won't carry your scholarly voice.The output is competent prose, not your argument.
Curating insight through your judgment is still the work.
The full
bandwidth
When you can read along.
Same question, visible process.
All three principles, alive at once.
The agent has a workshop.
The reasoning trace
becomes an artifact.
- Every choice the agent surfaced — and why.
- Mid-process intervention as a first-class move.
- All three principles, simultaneously.
And what it costs.
- Setup cost (files, tools, environment), which pays off in the long term.
- More surface area means more places to drift.
- More bandwidth ≠ better work. The principles still gate.
Not levels.
Bandwidths
The narrowest bandwidth, but everything important is on the page.
Not levels.
Bandwidths
Discovery you can't do alone, but the search itself stays opaque.
Not levels.
Bandwidths
Same principles. Same practice.
Synthesis
The question sharpened — and the sharpening is the finding.
When management researchers run audits, evaluations, or experiments on LLMs, what methods do they use and what kinds of claims do they make based on those methods?
A reasonable opening, but soft. Look at what it becomes.
When management researchers run audits, evaluations, or experiments on LLMs, what methods do they use and what kinds of claims do they make based on those methods?
When management researchers treat LLMs as cognitive entities — evaluators, decision-makers, reasoners — what evidence licenses those claims, and what kinds of claims can that evidence actually support?
The question you end with matters more than the question you start with.
From abstractions to craft.
Remember when I rewrote the prompt because my first version was too generic.
Remember when I took the deep-research report to a fresh session for interrogation.
Remember when the output went flat and I looked at my input instead of the model.
"The purpose of this talk is not to teach you the content. It's to change how you think about the process."
From abstractions to craft.
Remember when I rewrote the prompt because my first version was too generic.
Remember when I took the deep-research report to a fresh session for interrogation.
Remember when the output went flat and I looked at my input instead of the model.
"The purpose of this talk is not to teach you the content. It's to change how you think about the process."
From abstractions to craft.
Remember when I rewrote the prompt because my first version was too generic.
Remember when I took the deep-research report to a fresh session for interrogation.
Remember when the output went flat and I looked at my input instead of the model.
"The purpose of this talk is not to teach you the content. It's to change how you think about the process."
Interpretive
Orchestration
Lin, X. & Corley, K. G. (2026). Strategic Organization.
Three models. Each maps to one configuration you just saw.
One practice,
three strategic models.
From Lin & Corley, Interpretive Orchestration. The models are the practice; the configurations are how it shows up at different bandwidths.
Deliberate contradiction to surface assumptions.
Systematic context-building for reproducibility.
Reading across independent analytical passes.
Resources.
Start here · no setup
- Claude claude.ai
- Kimi kimi.ai
- ChatGPT chatgpt.com
- Gemini gemini.google.com
Go deeper
- Claude Code + Obsidian setup
- Interpretive Orchestration paper
papers.ssrn.com / abstract_id=6629679 - Markdown source on GitHub
github.com/linxule/interpretive-orchestration-paper
Supplementary readings
- Epistemic Voids #1 — Citation Theater threadcounts.org
- Research with AI #1 — The Foreclosure Problem threadcounts.org
- LOOM XVI — Are You Climbing the Right Hill? threadcounts.org
- Alvesson & Sandberg (2011), AMR 36(2), 247–271
The question you end with matters more than the question you start with.
AI-augmented methods accelerate that movement and make it more visible.
They demand deeper and more rigorous engagements from us.