I have argued in earlier posts that the ultimate arbiter of truth is reality itself. A lie or an untrue statement, if significant enough, will eventually be judged by how well it predicts what happens or has happened in the real world. Some poor predictions are inconsequential but some have dire consequences. If our model of the world says that avoiding measles vaccine is on balance beneficial, reality will with some probability correct us by hitting us, or our child, with measles. When markets tank suddenly the government may conclude that tariffs are a bad idea. Reality is the non-political supreme court of our beliefs and statements. We ignore it at our peril.
If we, like toddlers, don’t have any model of the world, we will have to test everything against reality. Toddlers hurt themselves from time to time even if we, the parents, try to protect them from the worst falls and mistakes. Eventually they will, based on their own mistakes and – perhaps to a lesser degree – based on listening to their parents, construct a useful model of the world, a model that will predict what will happen as a consequence of some action or some independent event in the environment. A better model of reality makes reality less surprising and therefore more pleasant.
I posit that we are not always great at learning from each other, together with each other, from history, or from other documented bodies of knowledge. We too often subject too poor models too soon to reality. An important reason is that we seem to have an aversion to inquiry.
Inquiry is here defined as a way to improve our model of the world by asking questions, before testing the model against reality. Inquiry improves our models or reality in many ways:
- They refine our mental models by exposing gaps, inconsistencies, or alternative perspectives.
- They accelerate learning by allowing us to tap into the knowledge of others.
- They improve social interactions by fostering connection and mutual understanding.
- They prepare us for reality’s feedback loop—even if we don’t ask questions, reality will still correct us. Inquiry helps us see what’s coming rather than being blindsided by it.
There are at least three scenarios in which asking more and better questions than we usually do would be beneficial:
- In social interactions
- When creating and compiling new knowledge at work
- Interacting with large language models
Inquiry in social interactions
Asking questions in a social setting, when talking with another person, we signal interest, respect, and curiosity about the other person’s experiences. Questions may lead to deeper and more meaningful relationships through learning about the other person.
The only question many people ask is a perfunctory “how are you?”. Many more are willing to share their own opinions and accomplishments unasked. Although there may be several reasons for the reluctance to ask questions (more on that below), I interpret such behavior as a missed opportunity to learn something new. On a more personal level it is also a sign of lack of interest in the other person.
Inquiry at work
Peter Senge has proposed something he calls skillful discussion as a means to come to decisions in a professional setting using all team members’ knowledge and insights. The objectives of skillful discussion are:
- Committed decisions or shared priorities
- All assumptions known
- One shared mental model (ontology)
There are many techniques involved in skillful discussion but an importan one is to balance advocacy and inquiry. Skillful discussion is not about trying to convince people but to find the truth or best way forward using all the knowledge of the participants. In a skillful discussion you can hear phrases like:
- What do you mean by…?
- How do you define?
- Is your assumption thus that…?
- Let me test if I understand…
Once the we know the mental models and assumptions we can discuss the merits of the different proposals productively. The key technique here is to ask questions.
Using large language models
A third setting where questions are increasingly crucial is in our interactions with large language models. LLMs contain a wealth of information. To find pertinent and true information, one must design and enter a good and precise prompt and ask good follow-up questions.
Why are we so bad at asking questions?
Ego and the desire to be heard: Many people prioritize expressing their own views over understanding others. Talking affirms their identity, beliefs, and social status, while asking questions can feel like deferring to someone else’s knowledge.
Cognitive laziness: Questions require cognitive effort (type 2 thinking) and cognitive dissonance which can be painful. It’s easier to default to existing assumptions than to challenge them through inquiry.
Social dynamics and power: In many social settings, asking questions can be perceived as weakness. Some people avoid asking because they don’t want to appear ignorant or subordinate. In contrast, making bold statements is seen as assertive and confident.
Lack of training in inquiry: Traditional education often focuses on answering rather than questioning. Students are rewarded for knowing the right answers, not for asking better questions. This conditions people to approach discussions as competitions rather than collaborative inquiries.
Psychological defense mechanisms: Asking questions invites uncertainty and cognitive dissonance. People avoid questioning deeply held beliefs because it might force them to confront uncomfortable truths.
Social media and the decline of dialogue: Online platforms encourage broadcasting opinions rather than engaging in back-and-forth inquiry. The format of social media rewards soundbites, not thoughtful questioning.
Cultural differences in communication: Some cultures emphasize individual expression over dialogue-based reasoning. In contrast, Socratic-style inquiry thrives in cultures that value collective wisdom and humility in the face of knowledge.
Lack of self-confidence: Asking questions without fear of appearing ignorant or incompetent requires self-confidence and psyhological safety. An organization based on fear does not promote knowledge creation through inquiry.
A way forward
Asking the right question or defining the right problem will be more important in the future than having the right answer or information. We therefore have to create contexts where inquiry is encouraged and we need to learn a new mindset and some new techniques to become good at asking the right questions. These are a few suggestions as to how we can become better at asking questions:
In social interactions
Be genuinely curious, not performatively polite: Ask questions that show you actually care about the person’s experience, not just to keep the conversation going.
Follow up on emotional cues: If someone shares something meaningful or vulnerable, don’t pivot—go deeper.
Ask questions that show you listened: Demonstrate attention and interest by asking about something the person said earlier.
At work
To create a work environment where inquiry flourishes, you need more than just intellectual tools—you need emotional safety and cultural reinforcement, a “fearless organization”.
Foster “confident humility” in team members: Encourage the mindset: “I know some things, and I’m still learning.” Reward curiosity and thoughtful questioning—not just answers and performance.
Normalize not knowing everything: Leaders should model vulnerability: “I don’t have the full picture—what do you see that I don’t?” This builds a climate where asking questions is a strength, not a weakness.
Decouple identity from knowledge: If people believe their value = knowing everything, they’ll avoid inquiry. Instead, build a culture where value = contribution + growth + collaboration.
Interacting with an AI
Frame your prompts as specific questions, not vague commands: Instead of: “Tell me about climate change”, try: “What are the key causal mechanisms linking fossil fuel use to global temperature rise?”.
Iterate and refine: Treat the first response as raw material. Ask follow-up questions to deepen or challenge the output. Example: “Can you give a historical example of that?” or “What are some counterarguments?”.
Ask from multiple perspectives: Prompt the AI to consider other lenses: “How would a behavioral economist see this?” or “What assumptions underlie this model?”.