Why does the other side suddenly know so much, and still not enough?

Because they are handed answers without the experience to place them. An AI delivers the most plausible average solution in seconds, in a tone that leaves no room for doubt. That gives the other side enough knowledge to hold a firm opinion and too little to judge its quality. The cluelessness has not disappeared, it has only stopped being visible from the outside.

The cluelessness has moved house

The customer has not become smarter. They have become clueless in a different way. They used to arrive with a problem and the honest admission that they could not solve it. Today they arrive with an answer they take to be the solution, in the confident tone of someone who believes they have done their homework.

The cluelessness has not disappeared; it has moved house: from the open "I don't know" to the concealed "I think I know." And concealed cluelessness is more expensive for both sides than the open kind, because it has to be uncovered before you can even begin.

Enough for an opinion, too little for a judgment

Let us name the phenomenon: half-knowledge. It is neither ignorance nor knowledge but the dangerous in-between. Enough to hold an opinion. Too little to judge quality.

The AI effortlessly hands the customer the most plausible average solution, not the best and not the one that fits their specific case, and it delivers it in a tone so assured that they take it for the truth. The customer now appears with a solution sketch that sounds plausible, with technical vocabulary they only partly grasp, with a price expectation drawn from AI ballpark figures, and with the expectation that you will quickly confirm or refute all of it, ideally for free.

Half-informed and AI-armed

Example: the appointment at the bank

Why the battle cry misleads

So why not use "the end of the clueless customer" as a battle cry after all? Because it lures the provider into the wrong posture. Whoever believes their counterpart is now informed treats them like an expert and is surprised when the collaboration fails. Whoever understands instead that their counterpart is half-knowing, that is, convinced and at the same time uncalibrated, knows what to do: not to explain what the other does not know, but to gently place what they believe they know. That is an entirely different activity, and the rest of this book is about it.

Borrowed knowledge

A useful extension of this diagnosis is the idea of borrowed knowledge. Borrowed does not mean false. An AI answer may be correct, useful, and far ahead of the user's own understanding. But it can be used before its assumptions, limits, provenance, and consequences have truly been absorbed. Access to answers has grown faster than the ability to judge their fit. A new confidence therefore appears at the surface while the experience, calibration, and responsibility that turn a plausible answer into sound judgment may still be missing underneath.

This does not affect customers alone. Providers, organizations, and agents also work with mixtures of lived experience, professional judgment, machine synthesis, and unexamined assumptions. The new gradient therefore no longer runs simply between those who possess information and those who do not. It runs between producing a convincing answer and placing that answer responsibly and carrying its consequences. Half-knowledge describes the visible condition; borrowed knowledge explains one of its most important pathways.

The Deep Currents of Intent

The visible conversation is only the surface. Fast-changing AI answers, terminology, tasks, and offers are shaped by slower objectives, authority, commitments, and values. In the other direction, experience, friction, and evidence travel back down and change, depending on their significance, the operational movement, the project, the strategy, or in rare cases even the enduring course.

flowchart BT
  B["GRUND / BEDROCK<br/>Identität · Würde · Werte · Zweck<br/>identity · dignity · values · purpose"]
  D["TIEFENSTRÖMUNGEN / DEEP CURRENTS<br/>langfristige Wirkungen · Verpflichtungen · rote Linien<br/>long-term outcomes · commitments · red lines"]
  M["MITTLERE STRÖMUNGEN / MID-WATER CURRENTS<br/>Strategie · Befugnis · Fähigkeiten · Regeln<br/>strategy · authority · capabilities · policies"]
  T["GEZEITEN / TIDES<br/>kurzfristige Ziele · Projekte · Budgets<br/>short-term objectives · projects · budgets"]
  W["WELLEN / WAVES<br/>Tagesziele · Aufgaben · Prompts · Angebote<br/>daily goals · tasks · prompts · offers"]
  F["SCHAUM / FOAM<br/>KI-Antworten · Begriffe · geliehenes Wissen<br/>AI answers · terminology · borrowed knowledge"]
  H["BEGEGNUNG AN DER OBERFLÄCHE / SURFACE HANDSHAKE<br/>Mensch ↔ Mensch · Agent ↔ Agent"]
  E["RÜCKSTRÖMUNG / DOWNWELLING<br/>Evidenz · Reibung · Ergebnisse · Lernen<br/>evidence · friction · outcomes · learning"]

  B -->|trägt / grounds| D
  D -->|richtet aus / directs| M
  M -->|befugt / authorizes| T
  T -->|fokussiert / focuses| W
  W -->|erzeugt / produces| F
  F --> H

  H -.->|was tatsächlich geschah / what happened| E
  E -.->|anpassen / adapt| W
  E -.->|überprüfen / reconsider| T
  E -.->|revidieren / revise| M
  E -.->|selten verwandeln / rarely transform| D

Before you pass on an AI answer, check two things: what it rests on and what you are left with if it is wrong. And tell your counterpart that the answer came from an AI. It costs you nothing and spares you both the round in which what you actually know has to be uncovered first.

• Next in the Guide: Chapter 5: What does this mean for you day to day?