Don’t trust the answer just because it sounds finished.
Do Not Trust is a practical book series for people who use AI but still want to own their judgment. Build with the machine. Turn it against the answer. Check what matters in the real world. Then decide.
The cost of sounding competent has collapsed.
AI can produce clean structure, confident tone and persuasive arguments in seconds. The old signal used to be simple: polished work usually meant someone had done the thinking. That signal no longer holds.
It looks right
A polished answer can feel like evidence that the work underneath was done. Today, polish can be manufactured before anyone has checked the claim.
It agrees with you
The most dangerous AI answer is often not the obviously wrong one. It is the one that confirms what you already wanted to believe.
It moves too fast
When coherent answers are produced faster than they are tested, beliefs start drifting away from reality without looking broken.
Build. Oppose. Look. Decide.
The method is deliberately simple. It is not another prompt trick. It is a repeatable discipline for keeping human judgment active while using AI.
Build
Use AI for what it is good at: structure, alternatives, drafts, summaries and first-pass reasoning.
Oppose
Turn the machine against the answer. Ask what is weak, hidden, missing or too convenient.
Look
Leave the words. Check the claim against a source, data, a person who knows, or a real-world consequence.
Decide
Do not let the machine own the conclusion. The final judgment remains yours, especially when it matters.
Not anti-AI. Anti-blind trust.
Do Not Trust starts from a simple position: AI is useful because it makes thinking faster, wider and easier to organise. That is exactly why it needs a counterweight.
The goal is not to make people use AI less. The goal is to make them use it with more responsibility, more friction at the right moments and a clearer sense of what must still be checked outside the chat.
Use AI for clarity without letting the first coherent answer become the final answer.
Use AI as a coach that tests understanding, not as a shortcut that hides weak learning.
Use AI to build faster, then red-team assumptions before a document, memo or model moves forward.
Use structured challenge, traceability and clear human ownership when the cost of being wrong is real.
Start with the generic edition.
The first release is written for any reader. It gives the public method, the BOLD loop and the working tools without turning the whole framework into a free website. The deeper domain versions sit behind the product.
Don’t Trust This Book
The general edition. A short, plain book about how to think with AI, test confident answers and keep your own judgment active.
- Generic PDF edition
- Readable in one sitting
- The BOLD thinking loop
- The BOLD Card and decision trace worksheet
- Access to future homepage resources
Domain bundles
For readers who want the same loop translated into the decisions they actually face.
- Business and organisations
- Parents and students
- Medicine and clinical reasoning
- Research and expert work
RoseGuard bridge
For organisations, the same philosophy becomes decision stress-testing: challenge the logic before the decision becomes expensive.
- AI-supported decision review
- Assumption stress-testing
- Decision traces
- Board or investment memo challenge
Same discipline. Different stakes.
The generic edition teaches the habit. The domain editions adapt it to the places where fluent answers can quietly become decisions.
Clinical reasoning
For contexts where an unchecked confident answer is not just a text error. It may affect a patient.
People who decide
For strategy, investment, governance and internal memos where polished logic can hide weak assumptions.
Parents and students
For families who want AI to strengthen learning instead of replacing the struggle that builds understanding.
Expert work
For readers who need sharper literature use, argument checking and a clear boundary between source and inference.
Three perspectives behind one method.
Do Not Trust was written by Rikard, Carl and Victor Rosenbacke. It brings together clinical reasoning, medical training, economics, governance, technology and the ordinary decisions where AI is already changing how people think.
Rikard Rosenbacke
Rikard’s work sits at the intersection of governance, technology and high-stakes decision-making. His PhD research examines trust, errors and heuristics in human-AI collaboration, especially where AI enters clinical judgment.
Carl Rosenbacke
Carl studies medicine at Lund University and brings a practical eye for how abstract reasoning methods become usable tools. His role is to turn the framework into clear steps that people can actually run.
Victor Rosenbacke
Victor studies medicine at Lund University and has an bachelor in economics background from Lund University School of Economics and Management. His focus is decision-making under uncertainty, especially when fluent AI output makes weak assumptions look stronger than they are.
The plain-language layer of a deeper programme.
The book is deliberately short. Under it sits a longer research track on human-AI reasoning, false confirmation, reflective interfaces, decision traces and epistemic control loops. The website shows the surface. The product teaches the operating habit.
Why sounding right is no longer enough
AI has made coherent answers cheap. The question is no longer whether an answer looks competent, but whether it has survived contact with reality.
A small loop for keeping judgment alive
Build the answer, oppose it, look outside the words, then decide. The third move is the one most people skip, and the one reality grades.
Show how the decision was reached
For serious decisions, the method becomes a short trace: what was built, what was challenged, what was checked and who owns the judgment.
Before you trust the book, ask the obvious questions.
Clear answers to the questions people will ask before buying.
Buy the generic edition. Then test it on your next real decision.
The promise is not that you should trust this book. The promise is that it gives you a way to decide what deserves trust after testing.