Thinking Slow, with AI

Tying the lessons from Daniel Kahneman to the current moment in AI

I read Daniel Kahneman’s Thinking, Fast and Slow about ten years ago. The idea that sticks with me to this day is that a lot of our thinking happens before we even notice we’ve started thinking.

Kahneman distinguishes between two modes of thought. System 1 is fast thinking: intuitive, automatic, and mostly effortless. It helps us recognize a face, avoid a pothole, or know that 2 x 2 is 4 without doing much math. System 2 is slow thinking: deliberate, effortful, and conscious. It shows up when a problem requires intentional thought. Most of the time, fast thinking leads.

That feels newly relevant now because AI has made a strange bargain available to us: We can get more work done that looks deliberate without having to do much deliberation ourselves.

The risk is that AI makes it even easier to skip thinking and harder to notice that we skipped it.

Ask your LLM for a strategy critique, a list of customer themes, or a better version of a messy one-pager, and the answer often arrives fast, fluent, and plausible. It can feel like the hard, intentional slow thinking has already happened.

System 2 is How You Focus Attention

I’m tempted to turn Kahneman into a simple speed argument: System 1 is fast, System 2 is slow, so better thinking means slowing down.

In real work, speed is not the enemy. Automaticity is.

Some decisions need to move quickly. Some AI outputs should be accepted with light review because the cost of being wrong is low. Not every AI interaction needs to be a philosophical audit.

In my view, the useful distinction is automatic versus deliberate. Slow thinking is about deciding where attention belongs. Is this a generation problem, a framing problem, an evidence problem, a tradeoff problem, or a responsibility problem?

AI can help with any of these. But accountability does not transfer. Your AI agent doesn’t own the bet, disappoint the customer, or explain to a board why the strategy was coherent but wrong.

This is the point where the essay gets practical for me. Personally, I try to use AI freely when I need breadth, comparison, or a first pass. I still try to slow down when the work needs deeper analysis or judgment.

Kahneman gives me a few ways to notice when that is happening.

AI Makes Easier Questions More Tempting

“This is the essence of intuitive heuristics: when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution.”

— Daniel Kahneman, Thinking, Fast and Slow

Kahneman describes a pattern he calls substitution: when we face a difficult question we often answer an easier one instead, usually without noticing the swap.

This is a place AI can trick us because it gives us good answers to adjacent questions. We ask, “What should we build next?” and it responds with a clean list of opportunities. But a list of ideas is not really an answer. The right question is probably about which pain point(s) should get fixed first.

Or we ask, “Can this strategy work?” AI can make the narrative clearer while helping us avoid the harder questions.

That is the trap: AI can improve the artifact while leaving the right decision untouched.

Writing at least forces you to think through the problem. AI can make the writing appear fluent before it’s thought through.

WYSIATI Becomes a Prompt Design Problem

“You cannot help dealing with the limited information you have as if it were all there is to know.”

— Daniel Kahneman, Thinking, Fast and Slow

Kahneman’s phrase “what you see is all there is” has always felt like one of the most useful warnings in the book. We build conclusions from the information available to us, then forget about the information that never entered the prompt.

The way we write AI prompts can mitigate this. Every prompt we give is essentially a frame telling the model exactly what information matters and what boundaries to treat as fixed. A better prompt makes the missing information harder to ignore. That has changed how I think about prompts.

For example, “Write a strategy for improving conversion” is weak because it assumes conversion is the goal and that the right next move is a strategy. It skips past the uncertainty.

A better prompt framing would sound more like this:

“Here is what we know about where customers drop off and here is what we don’t know. Here are the principles and drivers of customer engagement we have data to support. Give me three competing explanations for the drop-off, what evidence would support each one, and what small test would distinguish between them.”

Now, that prompt can still produce weak output, but at least it incorporates uncertainty and forces us to think more slowly about what we actually need answered.

This is one area where AI can strengthen slow thinking: it helps frame the need better before we fall in love with the answer.

Bringing an Outside View Matters More When You Get Answers Faster

Another Kahneman lesson that matters here is the planning fallacy. We tend to work from an inside view that underestimates how long things will take and overestimates how smoothly they go.

The inside view feels more informed. We know our team. We know our constraints. We know the plan. We know why this time might be different.

AI can make this worse because it’s so good at extending the story we already believe. If we give it our plan, assumptions, and desired outcome, it can make the plan clearer. But it’s simply working inside the frame we handed it.

I felt this during a roadmap planning exercise this spring. We had a strategy document, a set of proposed tactics, and a collection of team roadmaps. The question was simple: could we show real progress against the strategy before Q4?

I used AI to move through the material faster. It helped search, compare, and map the pieces across teams. But after hours of review, I never convincingly mapped the tactics cleanly to real impact. The outside view lowered my confidence, but in a useful way. The roadmap looked full, but fullness was not the same as value.

That is the outsider view. It asks: how do plans like this usually fail? In this case, they fail when a team can show a lot of activity but not that the effort adds up to the outcome.

The Advantage Is Knowing What Deserves to Go Slow

Kahneman’s warning was that we often start thinking before we realize it. AI makes it easier than ever to skip thinking things through while looking like we have.

I heard a version of this last week from an engineering lead helping his reports think about AI. He said, “AI is great at coding tasks. But it still cannot do engineering tasks well.”

A coding task can be specific, bounded, and reviewable. An engineering task often includes constraints, tradeoffs, system behavior, ownership, maintenance, and responsibility for what happens after the code is shipped.

For most knowledge workers, refusing AI will probably become an untenable position. But using it well is very different than using it everywhere. It’s more than being clever with prompts. The work is knowing when the answer has moved too fast for the thinking that should happen underneath it.

AI can make the work look thought-through before we have focused on it. The slow thinking still needs to belong to us.